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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2017.2663318, IEEE Internet ofThings Journal

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TRIEDS: Wireless Events Detection Through theWall

Qinyi Xu∗†, Student Member, IEEE, Yan Chen†‡, Senior Member IEEE, Beibei Wang∗†, Senior Member, IEEE,and K. J. Ray Liu∗†, Fellow, IEEE

∗University of Maryland, College Park, MD 20742 USA†Origin Wireless, Inc., Greenbelt, MD 20770 USA

‡University of Electronic Science and Technology of China, Chengdu, ChinaEmail:∗{qinyixu, bebewang, kjrliu}@umd.edu, ‡[email protected]

Abstract—In this work, we propose a novel wireless indoorevents detection system, TRIEDS. By leveraging the time-reversal(TR) technique to capture the changes of channel state infor-mation (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM bandto perform through-the-wall indoor multiple events detection.The multipath phenomenon denotes that the electromagneticsignals undergo different reflecting and scattering paths in arich-scattering environment. In TRIEDS, each indoor event isdetected by matching the instantaneous CSI to a multipath profilein a training database. To validate the feasibility of TRIEDS andto evaluate the performance, we build a prototype that workson ISM band with carrier frequency being 5.4 GHz and a 125MHZ bandwidth. Experiments are conducted to detect the statesof the indoor wooden doors. Experimental results show thatwith a single receiver (AP) and transmitter (client), TRIEDScan achieve a detection rate higher than 96.92% and a falsealarm rate smaller than 3.08% under either line-of-sight (LOS)or non-LOS transmission.

Index Terms—Indoor events detection, time reversal (TR),wireless events detection, spatial-temporal resonance, through thewall.

I. INTRODUCTION

The past few decades have witnessed the increase in thedemand of surveillance systems which aims to capture andto identify unauthorized individuals and events. With thedevelopment of technologies, traditional outdoor surveillancesystems become more compact and of low cost. In orderto guarantee the security in offices and residences, indoormonitoring systems are now ubiquitous and their demand isrising both in quality and quantity. For example, they can bedesigned to guard empty houses and to alarm when break-inhappens.

Currently, most indoor monitor systems basically rely onvideo recording and require cameras deployments in targetareas. Techniques in computer vision and image processing areapplied on the captured videos to extract information for realtime detection and analysis [1]–[4]. However, conventionalvision-based indoor monitor systems have many limitations.They cannot be installed in places requiring high level of pri-vacy like restrooms or fitting rooms. Owing to the prevalence

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of malicious softwares on the Internet, vision-based indoorsurveillance systems may lead to more dangers than pro-tections, contradicting their intention. Moreover, vision-basedapproaches have a fundamental requirement of a line-of-sight(LOS) environment with enough illumination is indispensable.

On the other hand, sensing with the wireless signals todetect indoor events has gained a lot of attention [5]. Byutilizing the fact that the received radio frequency (RF) signalscan be altered by the propagation environment, device-freeindoor sensing systems are capable of capturing activities inthe environment through the changes in received RF signals.Common features of RF signals to identify variations duringsignal transmission for indoor events detection include thereceived signal strength (RSS) and channel state information(CSI). Due to its susceptibility to the environmental changes,the RSS indicator (RSSI) has been applied to indicate andfurther recognize indoor activities [6]–[9]. Sigg et al. proposeda method that links the patterns of RSSI fluctuation to differenthuman activities [7]. An approach where the direction ofhuman movement was determined according to the RSSIdegradation among different receivers was proposed in [8].Recently, a RSSI-based gesture recognition system was builtwhere 7 gestures were identified with accuracy 56% [9].Furthermore, CSI information, including the amplitude andthe phase, is now accessible in many commercial devices andhas been used for indoor event detection [10]–[16]. In [10],the first two largest eigenvalues of CSI correlation matrix wereviewed as features to determine whether environment is staticor dynamic. Abid et al. applied MIMO interference nullingtechnique to eliminate reflections off static objects and focuson a moving target, and used beam steering and smoothedMUSIC algorithm to extract the angle information of target[11]. Han et al. treated the CSI in the 3 × 3 MIMO systemindependently and the standard deviations of the CSI werecombined with SVM for human activity detection [12]. In[13], in order to locate the client with a fixed AP, both theamplitudes of the CSI and the frequency diversity in OFDMspecturm were used to build a model for calculating thedistance between the AP and the client. In [14] the histogramsof the CSI amplitudes were utilized to distinguish betweendifferent human activities. In [15] a coarse relationship be-tween variation in CSI amplitudes and the number of personspresent was established. Wang et al. proposed the CARM that



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leveraged the CSI-speed and CSI-activity models for detection[16]. Moreover, a lip reading system based on WiFi signalswas developed where the features of mouth motions wereextracted through the discrete wavelet packet decompositionon CSI’s amplitudes and classified with the help of dynamictime wrapping [17]. However, most aforementioned CSI-basedindoor sensing systems rely on only the amplitudes of the CSI,whereas the phase information is discarded regardless of howinformative it is.

Another category of technologies in device-free indoormonitor systems is adopted from radar imaging technologyto track targets [18]–[21]. The radar technique can identifythe delays of sub-nanoseconds in the time-of-flight (ToF) ofwireless signals through different paths, by using the ultra-wideband (UWB) sensing. Hence, radar-based systems arecapable of separating the reflection from the moving objectbehind the walls against the reflections from walls or otherstatic objects [22]. However, the UWB transmission is im-practical in commercial indoor monitoring systems, becauseit requires specific hardwares for implementation. Recently,Katabi et al. proposed a new radar-based system to keep trackof different ToFs of reflected signals by leveraging a speciallydesigned frequency modulated carrier wave (FMCW) thatsweeps over different carrier frequencies [19]–[21]. However,their techniques consume over 1GHz bandwidth to sense theenvironment and only the images of result are obtained fromthe sensors, which requires further effort to detect the typesof indoor events.

The aforementioned device-free systems have limitationsin that they either require multiple antennas and dedicatedsensors or require LOS transmission environment and ultra-wideband to capture features that can guarantee the accuracyof detection. In contrast, in this work, we propose a time-reversal (TR) based wireless indoor events detection system,TRIEDS, capable of through-the-wall indoor events detectionswith only one pair of single-antenna devices. In the wirelesstransmission, the multipath is the propagation phenomenonthat the RF signals reaches the receiving antenna throughtwo or more different paths. TR technique treats each pathof the multipath channel in a rich scattering environmentas a widely distributed virtual antenna and provides a high-resolution spatial-temporal resonance, commonly known asthe focusing effect [23]. In physics, the TR spatial-temporalresonance can be viewed as the result of the resonance ofelectromagnetic (EM) field in response to the environment.When the propagation environment changes, the involvedmultipath signal varies correspondingly and consequently thespatial-temporal resonance also changes.

Taking use of the spatial-temporal resonance, a novel TR-based indoor localization approach, namely TRIPS, was re-cently proposed in [24]. By exploiting the unique location-specific characteristic of channel impulse response (CIR), TRcreates a spatial-temporal resonance that focuses the energyof the transmitted signal only on the intended location. TheTRIPS mapped the real physical location to the estimatedCSI through the spatial-temporal resonance. The TR indoorlocationing system was implemented on a WiFi platform, andthe concatenated CSI from a total equivalent bandwidth of

Fig. 1: Prototype of TRIEDS.

1 GHz has been treated as the location-specific fingerprints[25]. Through non-line-of-sight (NLOS) experiments, the WiFibased TR indoor locationing system achieved a perfect 5cmprecision with a single access point (AP). TR based indoorlocationing system was an active localization system in thatit required the object to be located to carry one of thetransmitting or receiving device, such that the difference inthe TR resonances between different locations of device islarge.

Based on a similar principle as TRIPS, we utilize the TRtechnique to capture the variations in the multipath CSI due todifferent indoor events, and propose TRIEDS for indoor eventdetection. More specifically, thanks to the nature of TR thatcaptures the variations in the CSI, maps different multipathprofiles of indoor events into separate points in the TR space,and compresses the complex-valued features into a real-valuedscalar called the spatial-temporal resonance strength, the pro-posed TRIEDS supports simplest detection and classificationalgorithms with a good performance. Compared with previousworks on indoor monitoring systems which require multipleantennas, dedicated sensors, ultra-wideband transmission orLOS environment, and rely on only the amplitude informationin the CSI, TRIEDS introduces a novel and practical solutionwhich can well support through-the-wall detection and onlyrequires low-complexity single-antenna hardware operating inthe ISM band. To demonstrate the capability of TRIEDS indetecting indoor events in real office environments, we builda prototype that operates at 5.4 GHz band with a bandwidthof 125 MHz, as shown in Fig. 1, and conduct extensiveexperiments in an office on the tenth floor of an sixteen-story building. During the experiments, we test the capabilityof TRIEDS of monitoring the states of multiple doors atdifferent locations simultaneously. Using only one pair ofsingle-antenna devices, TRIEDS could achieve perfect detec-tion in LOS scenario and near 100% accuracy in detectionwhen events happens in the absence of LOS path between thetransmitter (TX) and the receiver (RX).

The rest of the paper is organized as follows. In SectionII, the system overview for TRIEDS is briefly discussedand an introduction to TR technique is given. The detailsof how TRIEDS works are studied and analyzed in SectionIII, consisting of an offline training phase and an onlinetesting phase. Moreover, extensive experiments of TRIEDSin detecting indoor events in real office environments areconducted and the experimental results are investigated in



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Fig. 2: TR-based wireless communication.

Section IV. Based on the results in Section IV, we furtherdiscuss how the system parameters, human motions will affectthe accuracy of TRIEDS, as well as the potential applicationsand future work. Finally, conclusions are drawn in Section VI.

II. TRIEDS OVERVIEW

When an EM signal travels over the air in a rich-scatteringindoor environment, it encounters reflectors and scatters thatalter and attenuate signals differently. Consequently, the re-ceived signal at the receiving antenna is a combination ofmultiple altered copies of the same transmitted signal comingfrom different paths and suffering different delays. This phe-nomenon is well known as multipath propagation. In order todetect an indoor event, wireless sensors should be capableof tracking the targets against all other interferences. Theprevious indoor monitoring work can be categorized into twoclasses. The first class ignores the multipath effect and onlyuses a single-valued CSI feature like RSSIs for detection,which leads to the degradation of accuracy to some extent.On the other hand, the second class tries to separate differ-ent components in a multipath channel, by means of UWBtransmission and specially-designed modulated signals.

The previous work either views the multipath as the com-promise to the system or separates the components in themultipath CSI by radar-based techniques. As opposed to them,TRIEDS is proposed as a novel system that monitors anddetects different indoor events by utilizing TR technique. Thedetails of TR technique are discussed as follows.

A. Time-Reversal Technique

A typical TR wireless communication system is shown inFig. 2 [26]. During the channel probing phase, the transceiverB sends an impulse to the transceiver A, which gets anestimated CSI h(t) for the multipath channel between Aand B. Then, the corresponding TR signature is obtainedby time-reversing and conjugating the estimated CSI h(t) asg(t) = h∗(−t). During the second phase, the transceiver Atransmits back g(t) and generates a spatial-temporal resonanceat the transceiver B, by fully collecting and concentratingthe energy of multipath channel. The TR spatial-temporalresonance can be viewed as the resonance of EM field inresponse to the environment, also known as the TR focusingeffect [23].

As originally investigated in the phase compensation overtelephone line [27], TR technique was then extended to theacoustics [28]. The spatial-temporal resonance of the TR has

Fig. 3: Mapping between the CSI logical space and the time-reversal space.

been proposed as theory and validated through experiments inboth acoustic domain and RF domain [29]. In the RF domain,the property of TR spatial-temporal resonances of EM waveshave been studied in [30], [31]. Moreover, the TR techniquerelies on two assumptions, i.e., the channel reciprocity andthe channel stationarity. The channel reciprocity demonstratesthe phenomenon that the CSI for both the forward and thebackward links is highly correlated, whereas the channelstationarity requires that the CSI remains highly correlatedduring a certain period. Both of the assumptions were validatedin [26], [32] and [24], respectively.

In the indoor environment, there exists a large amount ofpropagation paths for EM signals due to the presence ofscatters and reflectors. As long as the indoor propagationenvironment changes, the received multipath profile variesaccordingly. As demonstrated in Fig. 3, each dot in the CSIlogical space represents an indoor event or location, which isuniquely determined by the multipath profile h. By taking atime-reverse and conjugate operation over the multipaths, thecorresponding TR signatures g are generated and the pointsin the CSI logical space as marked by “A”, “B”, and “C” aremapped into the TR space as “A′”, “B′”, and “C ′”. In theTR space, the similarity between two indoor events or indoorlocations is quantified by the strength of TR resonances. Thedefinition of TR resonating strength (TRRS) is given in (3),where h1 and h2 represent the multipath profiles in the CSIlogical space and g2 is the TR signature in the TR space.The higher the TRRS is, the more similar two points are inthe TR space. Similar events defined by a threshold on TRRSwill be treated as a single class in TRIEDS. Leveraging theTR technique, a centimeter-level accurate indoor locationingsystem, named as TRIPS, was proposed in [24]. In TRIPS,each of the indoor physical locations was mapped into a logicallocation in the TR space and can be easily separated andidentified using TRRSs. Taking the advantage of the TR spaceto separate multipath profiles with small differences, TRIEDSis capable of monitoring and detecting different indoor eventswith a high accuracy.

III. SYSTEM MODEL

In this part, we present a detailed introduction to the pro-posed TR based indoor events detection system, TRIEDS. The



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Fig. 4: An example of indoor CSI.

proposed TRIEDS exploits the intrinsic property of TR tech-nique that the spatial-temporal resonance fuses and compressesthe information of the multipath propagation environment. Toimplement the indoor events detection based on the TR spatial-temporal resonances, TRIEDS consists of two phases: theoffline training and the online testing. During the first phase,a training database is built by collecting the signature g ofeach indoor events through the TR channel probing phase.After training, in the second phase, TRIEDS estimates theinstantaneous multipath CSI h for current state and makes theprediction according to the signatures in the offline trainingdatabase by means of the strength of the generated spatial-temporal resonance. The detailed operations are discussed inthe followings.

A. Phase 1: Offline Training

As discussed above, TRIEDS leverages the unique indoormultipath profile and TR technique to distinguish and detectindoor events. During the offline training phase, we are goingto build a database where the multipath profiles of any targetsare collected and stored the corresponding TR signatures in theTR space. Unfortunately, due to noise and channel fading, theCSI from a specific state may slightly change over the time.To combat this kind of variations, for each state, we collectseveral instantaneous CSI samples to build the training set.

Specifically, for each indoor state Si ∈ D with D being thestate set, the corresponding training CSI is estimated and forma Hi as,

Hi = [hi,t0 , hi,t1 , · · · , hi,tN−1], (1)

where N is the size of the CSI samples for a training state.hi,tj represents the estimated CSI vector of state Si at time tjand Hi is named as the CSI matrix for state Si. An exampleof estimated indoor CSI obtained by the prototype in Fig. 1shown in Fig. 4, where the total length of the CSI is 30. FromFig. 4a, we can find out that there exist at least 10 to 15significant multipath components.

The corresponding TR signature matrix Gi can be obtainedby time-reversing the conjugated version of Hi as:

Gi = [gi,t0 , gi,t1 , · · · , gi,tN−1], (2)

where the TR signature gi,tj [k] = h∗i,tj

[L − k].Here, thesuperscript ∗ on a vector variable represents the conjugateoperator. L denotes the length of a CSI vectors and k denotesthe index of taps. Then the training database G is the collectionof Gi’s.

B. Phase 2: Online Testing

After constructing the training database G, TRIEDS isready for real-time indoor events detection. The indoor eventsdetection is indeed a classification problem. Our objectiveis to detect the state of indoor targets through evaluatingthe similarity between the testing TR signatures and the TRsignatures in the training database G. The raw CSI informationis complex-valued and of high dimensions, which compli-cates the detection problem and increases the computationalcomplexity if we directly treat the CSI as the feature. Totackle this problem, by leveraging the TR technique, weare able to naturally compress the dimensions of the CSIvectors through mapping them into the strength of the spatial-temporal resonances. The definition of the strength of thespatial-temporal resonance is given as follows.

Definition: The strength of the spatial-temporal resonanceT R(h1,h2) between two CSI samples h1 and h2 is definedas

T R(h1,h2) =

(max

i

∣∣∣(h1 ∗ g2)[i]∣∣∣√∑L−1

l=0 |h1[l]|2√∑L−1

l=0 |h2[l]|2

)2

, (3)

where “∗” denotes the convolution and g2 is the TR signatureof h2 as,

g2[k] = h∗2[L− k − 1], k = 0, 1, · · · , L− 1. (4)

When comparing two estimated multipath profiles, theyare first mapped into the TR space where each of them isrepresented as one TR signature. Then the TR spatial-temporalresonating strength is a metric that quantifies the similaritybetween these two multipath profiles in the mapped TR space.The higher the TRRS is, the more similar two multipathprofiles are in the TR space. The resonating strength defined in(3) is similar to the definition of cross-correlation coefficientbetween h1 and h2 as the inner product of h1 and h∗

2, whichis equivalent to (h1 ∗ g2)[L − 1]. However, the numerator in(3) is the maximal absolute value in the convolved sequence.This step is important, in terms of combating any possiblesynchronization error between two CSI estimations, e.g., thefirst several taps of CSI may be missed or added in differentmeasurements. Hence, due to its robustness to the synchro-nization errors in the CSI estimation, the TRRS is capable ofcapturing all the similarities between multipath CSI samplesand increasing the accuracy.

During the online monitoring phase, the receiver keepsmatching the current estimated CSI to the TR signatures inG to find the one that yields the strongest TR spatial-temporalresonance. The TRRS between the unknown testing CSI Hand state Si is defined as

T RSi(H) = maxh∈H

maxhi∈Hi

T R(h,hi), (5)

where H is a group of CSI samples assumed to be drawn fromthe same state as

H = [ht0 , ht1 , · · · , hi,tM−1], (6)

and M is the number of CSI samples in one testing group,similar to the N in the training phase defined in (1).



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Once we obtain the TRRS for each event, the most possiblestate for the testing CSI matrix H can be found by searchingfor the maximum among T RSi(H), ∀ i, as

S∗ = arg maxSi∈D

T RSi(H), (7)

The superscript ∗ on S denotes the optimal.Besides finding the most possible state S∗ by comparing the

TR spatial-temporal resonances, TRIEDS adopts a threshold-trigger mechanism, in order to avoid false alarms introducedby events outside of the state class D. TRIEDS reports achange of states to S∗ only if the TRRS T RS∗(H) reaches apredefined threshold γ.

S =

{S∗, if T RS∗(H) ≥ γ,

0, otherwise,(8)

where S = 0 means the state of current environment is notchanged, i.e., TRIEDS is not triggered for any trained statesin D. According to the aforementioned detection rule, a falsealarm for state Si happens whenever a CSI is detected as S =Si but it is not from state Si.

Although the algorithm for TRIEDS is simple, the accuracyof indoor events detection is high and its performance isvalidated through multiple experiments in the next section.

IV. EXPERIMENTAL EVALUATION

To empirically evaluate the performance of TRIEDS, weconduct several experiments for door states detection in a com-mercial office environment with different transmitter-receiverlocations.

To begin with, a simple LOS experiment for validatingthe feasibility of TRIEDS is conducted in a controlled en-vironment, with 7 transmitter locations, one receiver locationand two events. Then, the validation is further extended toboth LOS and non-line-of-sight (NLOS) cases in a controlledoffice environment with 3 receiver locations, 15 locations fortransmitter and 8 targeted doors made of wood. Meanwhile,experiments are conducted in an uncontrolled indoor envi-ronment during normal working hours with people around.Furthermore, the performance of the proposed TRIEDS isalso compared with that of the RSS-based indoor monitoringapproach, which can be easily extracted from the channelinformation and classified the using k-nearest neighbor (kNN)method. To further evaluate the accuracy of the proposedTRIEDS in real environments, the performance of TRIEDSwith intentional human movements is studied. Last but notleast, results of TRIEDS being as a guard system to secure aclosed room are discussed.

A. Experimental Setting

The prototype of the proposed TRIEDS requires one pair ofsingle-antenna transmitter and receiver that work on the ISMband with the carrier frequency being 5.4 GHz and a 125 MHzbandwidth. Moreover, during the experiment, the system runswith a channel probing interval around 20 millisecond (ms).A snapshot of the hardware device for TRIEDS is shown inFig. 1 with the antenna installed on the top of the radio box.

Fig. 5: Floorplan of the test environment.

(a) Radio station: receiver. (b) Radio station: transmitter.

Fig. 6: Experiment setting: the transmitter and the receiver.

The experiments are carried out in the offices at the 10th

floor in a commercial building of 16 floors in total. Theexperimental offices are surrounded by multiple offices andelevators. The detailed setup is shown the floorplan in Fig. 5where different dotted marks represent different locations forthe transmitters and the receivers. During the experiments, weare detecting the open/close states of multiple wooden doorslabeled as D1 to D8. Each location for the transmitter, markedas small round dots and labelled by“TX1” and “TX2”, areseparated by 0.5 meters, whereas the candidate locations forthe receiver are marked as large round dots by “A” to “D”. TheTX-RX locations include both LOS and NLOS transmissions.

In TRIEDS experiments, the receiver and the transmitter areplaced on the top of stands at the intended locations, with theheight from the ground being 4.3 ft and 3.6 ft respectively, asshown in Fig. 6a and Fig. 6b.

In all the experiments, we choose the number of the trainingCSI and the testing CSI to be N = 10 and M = 10 as definedin (1) and (6).

B. Feasibility Validation

To begin with, the feasibility for the proposed TRIEDSto detect indoor events is verified in a LOS case where the



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Location Index 1 2 3 4 5 6 7False Alarm Rate 0 0 0 0 0 0 0

Detection Rate 1 1 1 1 1 1 1

TABLE I: Performance of the proposed TRIEDS in easy case.

receiver is placed at the location “D” in Fig. 5, the transmitteris moving along the 7 purple dots in a vertical line in Fig. 5with the dot closest to the targeted door labeled as index “1”.Our task is to detect whether the wooden door D3 is close oropen.

The multipath CSI samples for D3 open and close areobtained through TR channel probing phase and the corre-sponding TR signatures are stored in the database. In thetesting phase, we keep listening to the multipath channel andmatching the collected testing CSI to the database for. Withany threshold γ smaller than 0.97, we can achieve the perfectdetection for all the 7 transmitter locations as in Table I.

In this case, the proposed TRIEDS indeed performs adetection for the events on the LOS path between the trans-mitter and the receiver. Through this simple experiment, wehave demonstrated the feasibility of TRIEDS to use the TRspatial-temporal resonance to capture the changes in the indoormultipath environment. Next, the performance of TRIEDSis further evaluated under more complicated changes of themultipath environment and with both LOS and NLOS TX-RXtransmissions.

C. Single Door Monitoring

In this part, the experiments are conducted to understandhow locations of the receiver, the transmitter and the targetedobjecs affect the performance of TRIEDS. The receiver isplaced at location “A”, “B” and “C”, whereas the transmitteris moving along the 15 locations marked by green dots andseparated by 0.5 meters in a horizontal line as shown in Fig. 5.The objective of TRIEDS is to monitor the states of woodendoor D1. During the experiment, for each location and eachindoor event, we measure 3000 samples of the CSI which lastsabout 5 minutes by using our built prototype, leading to a totalexperimental time to be 10 minutes for each TX-RX location.

Here, the location “A” (LOC A) represent a throuth-the-wall detection scenario in the absence of a LOS path betweenthe transmitter and the receiver, and between the receiver andwhere the indoor event happens. Under the case when thereceiver is at the location “B” (LOC B), there is always a LOSpath between the receiver and where the indoor event happens,since they are in the same room. However, the LOS pathbetween the transmitter and the receiver disappears regardingmost of the possible transmitter locations, and it exists onlyif the transmitter, the receiver and the door D1 form a line.However, the transmitter and the receiver always perform LOStransmission when the receiver is at the location “C” (LOCC). Meanwhile, the door D1 to be detected falls outside of theLOS link between the transmitter and the receiver.

1) LOC A: NLOS case: As we discussed above, when thereceiver is on LOC A, there is no LOS path between thereceiver and the transmitter, and the receiver and door D1 areisolated by walls. One example of the multipath CSI for the

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Fig. 8: Multipath profiles of door D1 under LOC B.

open and the close state of door D1 is shown in Fig. 7. In Fig. 7where only the amplitudes of the CSI are plotted, it is clear toobserve a change in how the energy is distributed on each tap.In the proposed TRIEDS, not only the amplitude informationbut also the phase for each tap is taken into consideration bymeans of the TR spatial-temporal resonance.

From the experiment, with a threshold γ no larger than 0.9,we can achieve a perfect detection rate and zero false alarmrate for all 15 transmitter locations. Hence, we can concludethat TRIEDS is capable of detecting an event in a NLOSenvironment with through-the-wall detection and the distancebetween the receiver and the transmitter has little effect on theperformance.

2) LOC B: LOS and NLOS case: When the receiver ison LOC B, as the transmitter moving from the location “1”to the location “4” (the 4th dot right to the one marked as“1”), the transmission scenario between the transmitter andthe receiver is NLOS due to the absence of a direct LOSlink. Then, the trasmission scenario become LOS, when thetransmitter is on the location “5” to the location “6”. Whenthe transmitter moves farther away (i.e., from the dot “7”),there is no LOS path again between the transmitter and thereceiver and the transmission scenario becomes NLOS. In Fig.8a and Fig. 8b, examples of the CSI for each event are plottedto demonstrate the changes in the amplitudes of the multipathprofile corresponding to the indoor event.



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Fig. 9: Multipath profiles (amplitude part) of door D1 underLOC C.

Considering the accuracy for TRIEDS, with a threshold γ ≤0.9, the detection rate for all 15 transmitter locations is higherthan 99.9%. Except when the transmitter is at the location“6”, the detection probability drops to 95.9%. Nevertheless,the corresponding false alarm rates are all below 0.1%. Sincethe experiment is carried out in a commercial office building,there exist outside activities that we cannot control but indeedchange the multipath CSI to fall out the collected indoorevents. So the reason for the detection probability at the 6th

location being 95.9% might be the existence of uncontrollableoutside activities. For example, the elevator running whichmay greatly change the outside multipath propagation becauseit is close to the environmental office and is made of metal.Moreover, generally, TRIEDS is robust to the various distancesbetween the transmitter, the receiver and where the indoorevent happens.

3) LOC C: LOS case: When the receiver is on LOC C,no matter which green dot the transmitter is on, they aretransmitting under LOS scenario, which leads to a dominantmultipath component exists in the multipath CSI.

The LOS transmission brings difficulties to indoor eventsdetection when event locates outside of the LOS path betweenthe transmitter and the receiver. The reason for that canbe decomposed into two parts. In the first place, in thisexperiment, the object door D1 is located parallel with thetransmission link between the transmitter and the receiver,and has little influence to the dominant LOS component inthe multipath profile. Secondly, since more energy is focusedon the LOS path dominant in the CSI, the other multipathcomponents that contain the event information are more noise-like and less informative. Hence, as most of the informationfor the event is buried in the CSI components with only afew energy, it is hard to detect an event happening outside thedirect link between the transmitter and the receiver in a LOS-dominant wireless system. This can be shown by an exampleof the multipath CSI with respect to the open and close statesof door D1 in Fig. 9, where the dominant path remains thesame and contains most of the energy in the CSI.

In the experiment, TRIEDS yields a 100% detection rateand a 0 false alarm rate for all the 15 transmitter locations

State index DescriptionS1 All the doors are open.

Si+1 Door Di close and the others open,∀i = 1, 2, · · · , 8.

TABLE II: State list for TRIEDS to detect.

with the threshold γ ≤ 0.93. The experimental result supportsour claim that the proposed TRIEDS can capture even minorchanges in the multipath profile by using TR technique.

D. TRIEDS in Controlled EnvironmentsIn the previous sections, we have validated the capability of

the proposed system of detecting two indoor events with bothLOS and NLOS transmission in controlled indoor environ-ments. In this part, we are going to study the performanceof TRIEDS in detecting multiple indoor events. Moreover,the performance comparison between the RSSI-based indoordetecting approach and the proposed TRIEDS is further inves-tigated.

In the experiment, the receiver is placed on either LOC Bor LOC C, whereas the transmitter moves and stops on everytwo green dots that are separated by 1 meter, named from“axis 1” to “axis 4” respectively. In total, we have 2 receiverlocations and 4 transmitter locations, i.e., 8 TX-RX locations.The objective of TRIEDS is to detect which wooden doorsamong D1 to D8 is closed versus all other doors are open,as labeled in Fig. 5. During the experiment, for each TX-RXlocation and each event, we measure 3000 CSI samples whichtakes approximately 5 minutes, leading to a total monitoringtime of 45 minutes. In Table II is the state table describing allthe indoor events in the experiment.

As we claimed and verified in the single-event detectionexperiment that the proposed TRIEDS can achieve highly ac-curate detection performance by utilizing the spatial-temporalresonance to capture changes in the multipath profiles. In thissection, we evaluate the capability of TRIEDS of detectingmultiple events in a controlled indoor environment. The perfor-mance analysis for normal office environment during workinghours will be discussed in Section IV-E.

1) Evaluations on LOC B: To begin with, the performanceof TRIEDS when the receiver is on LOC B is studied. In Fig.10, we show how the TRRS varies between different events.

Due to the fact that door D5 and D6 are close to eachother whereas they are far away to the receiver and thetransmitter, the introduced changes in the multipath profiles ofboth of them are similar. Consequently, the resonance strengthbetween states S6 and S7 is relatively higher than other off-diagonal elements, but it is still smaller than the diagonalones in Fig. 10 that represent the in-class resonance strength.Similar phenomenon happens between states S8 and S9.

In Fig. 11 and Fig. 12, examples of the receiver operatingcharacteristic (ROC) curves for detecting states of indoor doorsare plotted for both the proposed TRIEDS system and theconventional RSSI approach. Here, the legend “aixs i”, i =1, 2, 3, 4, denotes the location of transmitter to be on the (2 ∗i− 1)th green dot in Fig. 5.

As shown by Fig. 11 and Fig. 12, the proposed TRIEDS out-performs the RSSI-based approach in distinguishing between



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Fig. 11: ROC curve for distinguishing between S1 and S2

under LOC B.

one door is close (i.e., Si, i ≥ 1) versus all doors are open(i.e., S0), by achieving perfect detection and zero false alarmrate. Note that S9 is the state of door D8 which is blockedfrom the TX-RX link by a close office, as an example, Fig.11 demonstrates the superiority of TRIEDS in performing athrough-the-wall detection. Meanwhile, the performance of theRSSI-based approach degrades as the distance between wherethe indoor event happens and the TX-RX gets smaller. Byleveraging the TR technique, TRIEDS is capable of capturingthe changes in a multipath environment in a form of multi-dimensional and complex-valued vector with high degree offreedoms, and of distinguishing between different changes inthe TR spatial-temporal resonance domain. However, the RSSIbased approach tries to monitor the changes in the environmentthrough a real-valued scalar, which due to its dimension losesmost of the distinctive information.

Furthermore, the accuracy of detection of TRIEDS improvesas the distance between the transmitter and the receiverincreases. So does the RSSI-based method. The reason isthat when the transmitter and the receiver get far away, moreenergy will be distributed to the multipath components withlonger distance and thus the sensing system will have a largerconverage. The overall performance obtained by averaged onall possible events shows that TRIEDS outperforms the RSSIapproach in Table III.

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LOC B axis 1 axis 2 axis 3 axis 4Detection Rate 99.12 99.5 99.67 99.81TRIEDS (%)False Alarm 0.88 0.5 0.33 0.19TRIEDS (%)

Detection Rate 89.41 91.16 92.07 93.07RSSI (%)

False Alarm 10.59 8.84 7.93 6.93RSSI (%)

TABLE III: False alarm and detection probability for multi-event detection on LOC B in controlled environment.

2) Evaluations on LOC C: Experiments are further con-ducted to evaluate the performance of indoor multiple eventsdetection in a LOS transmission scenario by putting thereceiver on LOC C. In Fig. 13, we show the strengths ofthe TR spatial-temporal resonances between different indoorevents. When the receiver and the transmitter transmit in aLOS setting, the CSI is LOS-dominant such that the energyof the multipath profile is concentrated only on a few taps.It makes the coverage of TRIEDS shrink and degrades theperformance of TRIEDS, especially when the indoor eventshappen far from the TX-RX link as shown in Fig. 13.

Examples of ROC curves to illustrate the detection perfor-mance of both TRIEDS and the RSSI-based approach are plot-ted in Fig. 14 and Fig. 15. The performance of the proposedTRIEDS working in a LOS environment is similar to that ina NLOS environment. Generally, TRIEDS achieves a betteraccuracy for events detection with a lower false alarm rate,compared with the RSSI-based approach. In both scenarios,TRIEDS achieves almost perfect detection performance indifferentiating between Si, i ≥ 1 and S0. Moreover, the RSSImethod has a better accuracy in the LOS case than that in theNLOS case.

The corresponding overall performance comparison forTRIEDS and the RSSI-based method is shown in Table IV. Itis obvious that the farther the receiver and the transmitter areseparated, the better accuracy TRIEDS achieves. Moreover,compared with Table III, the accuracy of RSSI-based methodimproves a lot in LOS environment, whereas the one ofTRIEDS degrades slightly. Moreover, comparing the results inTable III and Table IV, the detection performance for TRIEDSdegrades a little when the receiver and the transmitter changethe transmission scheme from NLOS to LOS. Because of



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the dominant LOS path in LOS transmission, the ability toperceive multipath components which is far away from thedirect link degrades, leading to a worse detection accuracy.

E. TRIEDS in Normal Office Environments

In this section, we repeat the experiments in Section IV-Dduring working hours in weekdays where approximately 10individuals are working in the experiment area, and all officessurrounding and locating beneath or above the experimentalarea are occupied with uncontrollable individuals.

The proposed TRIEDS achieves similar accuracy comparedwith that of the controlled experiment in Section IV-D. Theoverall false alarm and the detection rate for TRIEDS and theRSSI-based approach are shown in the Table V and Table VI.

The results in Table V and Table VI are consistent withthe results in Table III and Table IV. The performance forTRIEDS is superior to that of the RSSI-based approach, byrealizing a better detection rate and a lower false alarm rate.Even in the dynamic environment, the proposed TRIEDS canmaintain a detection rate higher than 96.92% and a false alarmsmaller than 3.08% under the NLOS case, whereas a detectionrate higher than 97.89% and a false alarm smaller than 2.11%under the LOS case. Moreover, as the distance between thereceiver and the transmitter increases, the accuracy of both

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LOC C axis 1 axis 2 axis 3 axis 4Detection Rate 99.09 99.28 99.31 99.35TRIEDS (%)False Alarm 0.91 0.72 0.69 0.65TRIEDS (%)


False Alarm 2.76 2.34 2.2 2.12RSSI (%)

TABLE IV: False alarm and detection probability for multi-event detection on LOC C in controlled environment.

LOC B axis 1 axis 2 axis 3 axis 4Detection Rate 96.92 98.95 99.23 99.4TRIEDS (%)False Alarm 3.08 1.05 0.77 0.6TRIEDS (%)


False Alarm 7.5 5.84 5.23 4.64RSSI (%)

TABLE V: False alarm and detection probability for multi-event detection of TRIEDS in normal environment (LOC B).

LOC C axis 1 axis 2 axis 3 axis 4Detection Rate 97.89 98.94 99.18 99.36TRIEDS (%)False Alarm 2.11 1.06 0.82 0.64TRIEDS (%)


False Alarm 3.27 2.81 2.65 2.57RSSI (%)

TABLE VI: False alarm and detection probability for multi-event detection of TRIEDS in normal environment (LOC C).

methods improves. In the comparison of Table III, IV, V andVI, we claim that the proposed TRIEDS has a better toleranceto the environment dynamics.

F. TRIEDS with Intentional Human Movements

To investigate on the effects that the human movementshave on the performance of TRIEDS, we conduct experimentswith none, one and two individuals keep moving back andforth in the shaded area as Figure 16 shows. Meanwhile, thetransmitter is put on the purple dot and the receiver is on thegreen dot, detecting the states of two adjacent doors labeled



10

Fig. 16: Experiment setting for study on human movements.

as “D1” and “D2”. The list of door states is in Table VII. Foreach set of experiments, TRIEDS detects the states of the twodoors for 5 minutes during the normal working hours.

State 00 01 10 11D1 Open Open Close CloseD2 Open Close Open Close

TABLE VII: State list for study on human movements.

Interference caused by the human movements changes themultipath propagation environment and brings in the variationsin the TR spatial-temporal resonances during the monitoringprocess of TRIEDS. Fortunately, due to the mobility of human,the introduced interference keeps change and the durationfor each interference is short. To combat the resulted burstedvariations in the TRRSs, we adopt the majority vote methodcombined with a sliding window to smooth the detectionresults over time. Supposing we have the previous K − 1outputs S∗

k , k = t−K + 1, · · · , t− 1 and the current resultS∗t , then the decision for time stamp t is made by majority

vote over all S∗k , k = t−K+1, · · · , t, so on and so forth for

all t. K denotes the size of the sliding window for smoothing.

Experiment No HM One HM Two HMNo 97.75% 87.25% 79.58 %SmoothingWith 98.07% 94.37% 88.33 %Smoothing

TABLE VIII: Accuracy comparison of TRIEDS under humanmovements.

In Table VIII, we compare the average accuracy over allstates for TRIEDS with or without the smoothing algorithm inthe absence of human movements (HM), and in the presence ofthe intentional persistent human movements performed by oneindividual and two individuals. Here, the length of the slidingwindow is K = 20. First of all, the accuracy of TRIEDSreduces as the number of individuals increases, performingpersistent movements near the location of the indoor eventsto be detected, the transmitter and the receiver. Moreover,the adopted smoothing algorithm improves the robustness ofTRIEDS to human movements and enhances the accuracy by7% to 9% compared with that of the case without smoothing.Meanwhile, during the experiments, we also find that the mostvulnerable state is state “00” where all doors are open, suchthat with human movements TRIEDS is more likely to yielda false alarm than other states. The reason is that as human

Fig. 17: Experiment setting for guarding.

moves close to the door location, the human body, viewed asan obstacle at the door location, is similar to a close woodendoor, and hence the changes in the multipath CSI are alsosimilar, especially for D1.

G. TRIEDS for through-the-wall Guard

Unlike the previous experiments where we are trying todetect the door states, in this part, TRIBOD is functioning asa through-the-wall guard system. The objective for TRIEDSis to secure a target room through walls and to alarm not onlywhen the door state changes but also when unexpected humanmovements happen inside the secured room. The system setupis shown in Figure 17, where the secured room is shaded.

In this experiment, the transmitter and the receiver ofTRIEDS, marked as purple and green dots, are placed in tworooms respectively as shown in Figure 17. TRIEDS is aimed tomonitor and secure the room in the middle, which is shaded inlight blue color, and to report as soon as the door of the securedroom is open or someone is walking inside the secured room.TRIEDS only collects the training data for normal state, i.e.,door is closed and no one is walking inside the room. Thetraining database consists of 10 samples of the CSI. OnceTRIEDS starts monitoring, it will keep sensing the indoormultipath channel profile, and compare it with the trainingdatabase by computing the time reversal resonance strengthaccording to (3) and (5).

An example is shown in Figure 18, where we can see aclear cut between the normal state and the intruder state,and between the normal state and the state where someoneis walking inside the room. The threshold 1 is the thresholdfor detecting when the indoor states deviates from the normalstate, leading to a 100% detection rate and 0 false alarm.Whereas the threshold 2 is for differentiating between theintruder state( i.e., door is open) and the state when someoneis walking inside the secured room with the door is close,based on which TRIEDS only has 3% error by classifying thehuman activity state as the intruder state. Even with a single-class training dataset, TRIEDS is capable of distinguishingbetween different events and functioning as an alarm systemto secure the rooms through the walls.

V. DISCUSSION

A. Experimental Parameters

1) Sampling Frequency: In this work, the sampling fre-quency of TRIEDS is 50 Hz, i.e., TRIEDS senses



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Fig. 18: Resonating strength of guard system.

the multipath environment every 20 ms. Since usuallythe changes of door states happen in 1 to 2 seconds(s), current sampling frequency is enough for capturingbinary changes for doors. In order to detect and monitorthe entire transition of the changes or other changeshappen in a sudden, a higher sampling frequency isindispensable.

2) Size of Training and Testing Group: In the currentexperiments, we choose both the training group size Min (1) and the testing group size N in (6) as 10, toaddress the variations of noise in the CSI estimation. Wehave studied the performance of TRIEDS with differentsizes of training and testing group. It is found out thatwith a size greater than 10, the performance does notimprove much but a larger delay for acquiring moreCSI samples is introduced. Hence, in this work, withoutsacrificing the time sensitivity of TRIEDS, the size of10 (i.e., a sensing duration of 0.2s) is adopted.

B. Impact of Human Movements

TRIEDS utilizes the TR technique to map multipath profilesof indoor events into separate points in the TR space, due tothe fact that different indoor events and human movementsalter the wireless multipath profiles differently.

In Section IV-G, the experimental results of applyingTRIEDS in a through-the-wall guard task are discussed. Asshown in the Fig. 18, in most cases, given the door closeevent with no human motions, the TRRS of the same eventwith human motions drops. However, the degradation in theTRRS introduced by human motions is small, whereas the gapbetween the TRRS of the door close event and that of the dooropen event is significantly large. The reason is that due to thesmall size of human body compared to indoor objects likedoors, human body only alters a small portion of multipathcomponents when moving not close to the transmitter orthe receiver, resulting in sparse changes in the amplitude orthe phase of a couple of taps in the CSI. Consequently, thepoint of door close event with human motions locates at the“proximity” of the point of the static door close event, i.e.,the two points are quite similar measured by the TRRS. They

can be viewed as a single cluster given a proper threshold onthe TRRS. However, when the human motions are close to thetransmitter or the receiver, there is a chance that the alteredmultipath profile differs a lot from the one of the static indoorevent, leading to a great attenuation in the TRRS, and thus adifferent cluster in the TR space as well as a miss detection inTRIEDS. Moreover, as discussed in Section IV-F, the detectionaccuracy drops compared to the case without intentionalmotions with intentional human movements. It is because thatdue to the existence of moving human bodies, the CSI orthe multipath profiles in the environment deviate accordinglyand keep changing. However, with the help of smoothing overthe time domain, the dynamic changes in multipath profilesintroduced by human motions can be trimmed out.

C. Future Work

This work validates the feasibility and capability of TRIED-S in detecting indoor events and evaluates its performancethrough experiments in real environments. We also recognizeseveral limitations of the existing system and potential appli-cations that motivate future work.

1) In this work, the capability of the proposed TRIEDSis only validated and evaluated through the experimentsto detect the states of multiple doors with the existenceof human movements in an office environment. In fact,TRIEDS is suitable for many other indoor events, suchas monitoring the states of windows, and differentiatingbetween different human movements. In the next step,we are going to conduct more experiments on detectingother events.

2) As the first to apply time-reversal (TR) technique toindoor event detections, this work is aimed to illustratethe feasibility and capability of TRIEDS in detectingevents in indoor environments with the simplest trainingand testing mechanism to produce acceptable results andperformance. Moreover, a prototype of the TR indoorevent detection system is built and put into experimentsin real indoor environments to test the performanceof TRIEDS. Advanced training and testing algorithms,e.g., the machine learning technique, will improve theperformance of the TR indoor event detection system.However, this is beyond the scope of this paper and weplan to investigate it in the next step.

3) Equipped with only one pair of the transmitter and thereceiver, the current system can yield a good detectionaccuracy for indoor events. However, by deploying moretransceiver pairs, the performance of TRIEDS can beimproved as the captured multipath profiles containinformation with more degrees of freedoms coming fromthe spatial diversity. We plan to explore the use ofmultiple transmitters or receivers to acquire the gain inspatial diversity for further performance improvement ofTRIEDS.

In spite of these limitations, we believe that the proposedTRIEDS introduce a novel idea to apply the TR techniqueto capture the variations in the multipath propagation environ-ments for future surveillance systems.



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VI. CONCLUSIONS

In this paper, we proposed a novel wireless indoor eventsdetection system, TRIEDS, by leveraging the TR technique tocapture changes in the indoor multipath environment. TRIEDSenables low-complexity devices with the single antenna, op-erating in the ISM band to detect indoor events even throughthe walls. TRIEDS utilizes the TR spatial-temporal resonancesto capture the changes in the EM propagation environmentand naturally compresses the high-dimensional features bymapping multipath profiles into the TR space, enabling theimplementation of simple and fast detection algorithms. More-over, we built a real prototype to validate the feasibility and toevaluate the performance of the proposed system. Accordingto the experimental results for detecting the states of woodendoors in both controlled and dynamic environments, TRIEDScan achieve a detection rate over 96.92% while maintaininga false alarm rate smaller than 3.08% under both LOS andNLOS transmissions.

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Qinyi Xu (S’15) received her B.S. degree with high-est honor in information engineering from SoutheastUniversity, Nanjing, China, in 2013. She is currentlyworking toward the Ph.D. degree in the Departmentof Electrical and Computer Engineering, Universityof Maryland College Park, MD, USA. Her curren-t research interests include signal processing andwireless communications.

She is a recipient of the Clark School Distin-guished Graduate Fellowships from University ofMaryland, College Park, and the Graduate with

Honor Award from Southeast University in 2013. Miss Xu was an exchangestudent at KTH-Royal Institute of Technology, Stockholm, Sweden from Aug.2012 to Jan. 2013, with the national sponsorship of China.

Yan Chen (SM’14) received the bachelor’s degreefrom the University of Science and Technology ofChina in 2004, the M.Phil. degree from the HongKong University of Science and Technology in 2007,and the Ph.D. degree from the University of Mary-land, College Park, MD, USA, in 2011. Being aFounding Member, he joined Origin Wireless Inc.as a Principal Technologist in 2013. He is currentlya Professor with the University of Electronic Scienceand Technology of China. His research interestsinclude multimedia, signal processing, game theory,

and wireless communications.He was the recipient of multiple honors and awards, including the Best

Student Paper Award at the IEEE ICASSP in 2016, the best paper award at theIEEE GLOBECOM in 2013, the Future Faculty Fellowship and DistinguishedDissertation Fellowship Honorable Mention from the Department of Electricaland Computer Engineering in 2010 and 2011, the Finalist of the Dean’sDoctoral Research Award from the A. James Clark School of Engineering,the University of Maryland in 2011, and the Chinese Government Award foroutstanding students abroad in 2010.

Beibei Wang (SM’15) received the B.S. degree(highest honor) in electrical engineering from theUniversity of Science and Technology of China,Hefei, in 2004, and the Ph.D. degree in electrical en-gineering from the University of Maryland, CollegePark, in 2009. She was with the University of Mary-land as a Research Associate from 2009 to 2010,and with Qualcomm Research and Developmentfrom 2010 to 2014. Since 2015, she has been withOrigin Wireless Inc. as a Principle Technologist. Herresearch interests include wireless communications

and signal processing. She received the Graduate School Fellowship, theFuture Faculty Fellowship, and the Dean’s Doctoral Research Award from theUniversity of Maryland, and the Overview Paper Award from IEEE SignalProcessing Society in 2015. She is a co-author of Cognitive Radio Networkingand Security: A Game-Theoretic View (Cambridge University Press, 2010).

K. J. Ray Liu (F’03) was named a DistinguishedScholar-Teacher of University of Maryland, Col-lege Park, in 2007, where he is Christine KimEminent Professor of Information Technology. Heleads the Maryland Signals and Information Groupconducting research encompassing broad areas ofinformation and communications technology withrecent focus on smart radios for smart life.

Dr. Liu was a recipient of the 2016 IEEE LeonK. Kirchmayer Technical Field Award on graduateteaching and mentoring, IEEE Signal Processing

Society 2014 Society Award, and IEEE Signal Processing Society 2009Technical Achievement Award. Recognized by Thomson Reuters as a HighlyCited Researcher, he is a Fellow of IEEE and AAAS.

Dr. Liu is a member of IEEE Board of Director. He was President ofIEEE Signal Processing Society, where he has served as Vice President –Publications and Board of Governor. He has also served as the Editor-in-Chief of IEEE Signal Processing Magazine.

He also received teaching and research recognitions from University ofMaryland including university-level Invention of the Year Award; and college-level Poole and Kent Senior Faculty Teaching Award, Outstanding FacultyResearch Award, and Outstanding Faculty Service Award, all from A. JamesClark School of Engineering.

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