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Blind GPS Receiver with aModified Despreader forInterference Suppression

SUK-SEUNG HWANG

JOHN J. SHYNKUniversity of CaliforniaSanta Barbara

The Global Positioning System (GPS) was designed to provide

location estimates for various civilian and military applications

using at least four satellites. Since GPS signals have a low

signal-to-noise ratio (SNR), they also have a low signal-to-jammer

ratio so that the accuracy of location estimates is influenced by

cochannel interference and intentional jammers. We propose

a low-complexity blind adaptive receiver that is based on a

novel modified despreader and the constant modulus (CM)

array. This system is capable of nulling directional interference

and capturing the GPS signal of interest without requiring

explicit angle-of-arrival (AOA) information. We also consider the

multiple satellite problem and extend the proposed receiver to

capture several GPS signals of interest. Representative computer

simulation examples are presented to illustrate the performance

of the multicomponent system for the suppression of different

jammer types.

Manuscript received March 6, 2005; revised July 25 and October 7,2005; released for publication November 2, 2005.

IEEE Log No. T-AES/42/2/876429.

Refereeing of this contribution was handled by G. Lachapelle.

Authors’ address: Dept. of Electrical and Computer Engineering,University of California, Santa Barbara, CA 93106-9560, E-mail:([email protected]).

0018-9251/06/$17.00 c° 2006 IEEE

I. INTRODUCTION

Twenty-eight GPS satellites operate in sixcircular orbits for use in location estimation, eachof which transmits two types of signals: the L1signal modulated by the coarse acquisition (C=A)code and the long precision (P) code, and the L2signal modulated only by the P code [1]. Thelarge spreading gain is adequate to compensate forthe low received SNR, but high-power jammingsignals and cochannel interference are increasinglybecoming a potential problem. The jamming signalsin the GPS environment can take on a variety ofdifferent types, from continuous waveform (CW)and frequency modulated (FM) signals to wideband(WB) noise. Potential interference sources can arisefrom transmissions at frequencies close to the GPSfrequencies, and from signals with a harmonic in theGPS band [2].The effects of different types of interference,

such as coherent CW and broadband signals, aswell as pulsed and continuous signals have beenstudied in [3]. That paper also considers various teststatistics such as the correlator output power, thecorrelator output variance, carrier phase vacillation,and an active gain controller (AGC). The jammingthreshold and jamming effectiveness based ondifferent jamming strategies were analyzed and testedin [4]. For narrowband jamming signal rejectionin a GPS receiver, a precorrelation temporal filtersuch as a notch filter was considered in [5] and [6].In the case of an FM jammer, which is basicallya narrowband signal in the GPS environment, asuppression technique based on a subspace projectionwas recently studied in [7] and [8].Adaptive space-time processors for WB jammers

were examined in [9] and [10]. To generatean approximation of the optimum beamformerin a GPS receiver, an interference-blockingmaximum SNR (IB-MSNR) beamformer usinga singular value decomposition (SVD) and aNewton-Raphson iterative search algorithm wereproposed in [11] and [12], respectively. Space-timeGPS signal processing techniques with designcriteria for a conventional matched filter (MF), aminimum-variance-distortionless-response (MVDR)beamformer, and an auxiliary-vector (AV) approachwere investigated in [13].For directional WB and narrowband interference

suppression, the MVDR beamformer was consideredin [14] and [15]. Although these systems provideexcellent performance for jammer rejection, thealgorithms require knowledge of the GPS signalangles of arrival (AOAs) and a model for the antennaarray, and they have a high computational complexitybecause a correlation matrix is computed from thereceived signal. In order to overcome their sensitivityto estimation errors in the GPS signal AOA, we

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proposed a blind multicomponent system based onthe multistage MF and constant modulus (CM) arrayin [16]. While this system does not require explicitAOA estimation for the GPS signal, it can have a highcomputational complexity for estimating the AOAs ofhigh-power jamming signals.In order to reduce this complexity and without

the need for AOA estimation for the GPS signal, werecently proposed in [17] a blind interference cancelerbased on a multistage version of the blind CM array[18]. The multistage CM array operates blindlyusing stochastic gradient techniques such as the CMalgorithm (CMA) [19] and the least-mean-square(LMS) algorithm [20]. However, since this previousreceiver must use a multistage version of the CMarray for multiple jamming signals, it is not ascomputationally efficient as a one-stage CM arrayimplementation.We propose a more efficient and simpler

interference rejection system consisting of a novelmodified despreader, followed by a one-stage CMarray and the GPS decision device, which is asignum-function detector. When the received signal isdespread, the modified C=A code transforms the CMjammers into non-CM signals, while retaining the CMcharacteristic of the GPS signal [21]. Therefore, onlyone CM array stage is needed to null the jammingsignals and capture the GPS signal of interest afterdespreading.For the best location estimation accuracy, GPS

requires at least four satellites with three satellitesequally spaced on the horizon at low elevation angles,and with the fourth satellite directly overhead [22]. Inthe work presented here, we also explore an extensionof the modified despreader system to handle multipleGPS signals [23]. The multistage CM array is usedto null the jamming signals and capture the GPSsignals after despreading. The MVDR beamformerfor the multiple satellite problem was previouslyconsidered in [14]. However, that approach has adramatic increase in the computational complexitycompared with the single satellite case, and requiresmultiple despreaders for the satellite signals. Theproposed system here requires only one despreaderand the multistage CM array, so it is considerablymore efficient than previously proposed systems forcapturing multiple GPS signals.The rest of this paper is organized as follows.

In Section II, we define the received signal modelfor GPS and the interference signals in additivewhite Gaussian noise (AWGN). In Section III, themulticomponent interference rejection system basedon the modified despreader and one-stage CM array isdescribed. The extended system for detecting multipleGPS signals is considered in Section IV. Computersimulations are provided in Section V to demonstratethe performance of the proposed system for the

suppression of different jammer types. Finally, theconclusions of this work are outlined in Section VI.

II. SIGNAL MODEL

GPS employs the C=A code to modulate the L1signal or the long precision (P) code to modulatethe L2 signal [22]. In this work, we only considerdetection of the C=A code, which employs binaryphase-shift keying (BPSK) and thus has the CMcharacteristic. Assuming that M elements areemployed in the receiver and the ith satellite signal isof interest, the received signal vector at sample instantk can be modeled as

x(k) = acci(k)b(k) +Aj(k) + v(k)

= acci(k)b(k) +ACMjCM(k) +AWBjWB(k)+ v(k)

(1)

where ac is the array response vector (size M) for theGPS signal, ci(k) is an element of the cyclostationarypseudorandom noise (PRN) code with length N =20£ 1023 for the ith satellite, and b(k) is the GPSdata bit which remains constant over the length ofone cycle of the PRN code. A is the M £L arrayresponse matrix, L is the number of interferers,j(k) is the vector (size L) of jammers, and v(k) isan AWGN vector (size M) with independent andidentically distributed components, each with zeromean and variance ¾2. ACM and AWB are the M £LCMand M £LWB array response matrices for the CMjammers and the WB noise jammers, respectively,LCM and LWB are the numbers of CM and WBnoise jammers, respectively, and the vectors jCM(size LCM) and jWB (size LWB) contain the CM andWB jammers, respectively. In this paper, we assumethat all non-CM jamming signals are WB noisejammers.For a receiver with a grid antenna array of size

P£Q (M = PQ), the lth column of the array responsematrix is given by [24]

al¢=

2666666666666666664

1

e¡j³l...

e¡j(Q¡1)³l

e¡j´l

e¡j(³l+´l)

...

e¡j((Q¡1)³l+(P¡1)´l)

3777777777777777775

(2)

where ³l¢=(d=¸)2¼ sinµl cosÁl, ´l

¢=(d=¸)2¼ sinµl sinÁl,

d is the interelement spacing, ¸= d=2 is the

504 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 42, NO. 2 APRIL 2006


Fig. 1. System architecture of GPS receiver for single satellite.

wavelength of the signals, µl is the elevation angle,and Ál is the azimuth angle, all for the lth signal. Thewavelength of the signals is twice the interelementspacing, which is a common assumption for anadaptive array. The components of the vector jCM(k)¢=[j1(k), : : : ,jLCM(k)]

T comprise a set of LCM CMjammers, and each signal can be modeled as one oftwo types: a CW signal jlCM(k) = expj(2¼flCMk+ÃlCM)where flCM and ÃlCM are the frequency and phaseof the lCMth signal, respectively, or an FM signaljlCM(k) = expj(2¼flCMk+¯ sin(2¼fmk)) where ¯ and fmare the modulation index and frequency, respectively.Note that for convenience here we utilize a cosineas the message signal for the FM jammers. A chirpor frequency-hopping jammer, where the centerfrequency of a CW signal varies over time, can beclassified as a CM jammer for our purposes becauseits magnitude is constant. The components of thevector jWB(k)

¢=[j1(k), : : : ,jLWB(k)]

T comprise a set ofLWB WB jammers, and each signal can be modeled asadditive Gaussian noise (AGN) with a bandwidth notexceeding that of the GPS signal. Finally, we considerpulsed jammers, which are periodic on/off signals andclassified as WB jammers [16].

III. INTERFERENCE REJECTION SYSTEM

The proposed system consists of the modifieddespreader, a one-stage CM array, and the GPSdecision device, with the architecture shown in Fig. 1.The modified despreader is designed to transform alljammers into non-CM signals, while the GPS signalof interest retains the CM property. The output of themodified despreader is processed by a one-stage CMarray that nulls the transformed jammers and extractsthe GPS signal.

A. Modified Despreader

Each satellite has a unique PRN code consisting oftwenty identical C=A codes. For the ith satellite, thePRN code can be written as [25]

ci = [cai, : : : ,cai]T (3)

where cai is the C=A code (length-1023 row vector).Since cTi ci =N, the output of the despreader based onci can be expressed as

x(n) = acNb(n)+ACMjCM(n) +AWBjWB(n) + v(n)

(4)

where jCM(n)¢=JCM(n)ci, JCM(n)

¢=[jCM(k), : : : ,

jCM(k+N ¡ 1)], jWB(n)¢=JWB(n)ci, JWB(n)

¢=

[jWB(k), : : : ,jWB(k+N ¡ 1)], v(n)¢=V(n)ci, and

V(n)¢=[v(k), : : : ,v(k+N ¡ 1)]. Note that Nb(n) and

jCM(n) have the CM characteristic.Next, we define the modified PRN code

ci¢=[cai,1, : : : ,cai,20]

T (5)

where cai,l (l = 1, : : : ,20) is randomly chosen fromthe C=A codes, excluding cai. Since c

Ti ci =¡20,

the output of a despreader using ci can be writtenas

x(n) =¡20acb(n) +ACMjCM(n) +AWBjWB(n)+ v(n)(6)

where jCM(n)¢=JCM(n)ci, jWB(n)

¢=JWB(n)ci, and

v(n)¢=V(n)ci. Note that only the term ¡20b(n) in (6)

has the CM property.

Defining y(n)¢=x(n)¡ x(n) and ci

¢=ci¡ ci, we can

write

y(n) = ac(N +20)b(n) +ACM jCM(n) +AWBjWB(n) + v(n)

(7)

where jCM(n)¢=JCM(n)ci, jWB(n)

¢=JWB(n)ci, and

v(n)¢=V(n)ci. Since only (N +20)b(n) in (7) has the

CM characteristic, the GPS signal can be capturedby a one-stage CM array. The third term in (7)obviously contains non-CM signals, and the secondterm is investigated below. Note that even if weutilize an arbitrary number of identical C=A codesinstead of twenty, the modified despreadertransforms the CM jammers into non-CM signalsbecause the modified PRN code varies with thetime index n. The CM characteristic of theGPS signal is retained for the other modifieddespreaders.

B. Operation of Modified Despreader

Next, we explore how the modified despreadertransforms the CM jammers into non-CM signalswhile retaining the CM property of the GPS signalof interest. Consider the lth CM jamming signal of thesecond term in (7):

aCM,l jCM,l(n) = aCM,ljTCM,l(n)ci (8)

where aCM,l is the array response vector for thelth CM jamming signal, jCM,l(n)

¢=[jCM,l(k), : : : ,

jCM,l(k+N ¡ 1)]T, and jCM,l(k) is the lth CM jammingsignal. The magnitude squared of (8) (ignoring

HWANG & SHYNK: BLIND GPS RECEIVER WITH A MODIFIED DESPREADER FOR INTERFERENCE SUPPRESSION 505


the array response vector) is given by

j¤CM,l(n)jCM,l(n) = cTi j¤CM,l(n)j

TCM,l(n)ci

=N¡1Xk=0

N¡1Xk0=0

j¤CM,l(k)jCM,l(k0)ci(k)ci(k

0)

=N¡1Xk=0

N¡1Xk0=0

j¤CM,l(k)jCM,l(k0)ci(k)ci(k

0)

+N¡1Xk=0

N¡1Xk0=0

j¤CM,l(k)jCM,l(k0)³ci(k,k

0)

(9)

where ³ci(k,k0)¢= ci(k)ci(k

0)¡ c(k)ci(k0)¡ ci(k)ci(k0),and the superscript ¤ denotes complex conjugation.We assume the worst case scenario where the sampleperiod N of jCM,l(k) is the same as that of the PRNcode of the GPS signals. Since ci is fixed for all nand the component ci(k) is a periodic sequence withperiod 1023, the first summation is constant for alln. However, the second summation is not constantbecause ci is randomly chosen from the C=A codesand thus is different for each n so that its componentci(k) is an aperiodic sequence. Therefore, (9) isnot constant and the CM jamming signals after themodified despreader no longer have the CM property.

C. One-Stage CM Array

A one-stage CM array is used to extract the GPSsignal of interest while nulling all the (modified)interference signals. The CM array output can bewritten as

d(n) =wHc (n)y(n) (10)

with weight vector wc(n) updated by CMA as follows[19]:

wc(n+1) =wc(n) +2¹y(n)"¤(n) (11)

where"(n)

¢=d(n)=jd(n)j ¡d(n) (12)

and ¹ > 0 is a step-size parameter that controls theconvergence rate.

D. CM Array Wiener Weight Vector

Next, we derive the Wiener weight vector of thissystem in order to investigate its steady-state (optimal)performance. The output of the CM array after themodified despreader is given by

do(n) =wHo y(n) (13)

where wo is the Wiener weight vector for extractingthe GPS signal of interest. The corresponding

estimation error can be written as

"o(n) = b(n)cTi ci¡ do(n)

= b(n)(N +20)¡do(n): (14)

Applying the orthogonality principle [20]E[y(n)"¤o(n)] = E[y(n)(b(n)(N +20)¡ do(n))¤] = 0, weobtain

E[y(n)b(n)(N +20)] = E[y(n)yH(n)]wo (15)

which yields the Wiener weight vector

wo = (N +20)2R¡1y ac (16)

where Ry¢=E[y(n)yH(n)] is the autocorrelation matrix

of the modified despreader output. In order to obtain(16), we have used the fact that the fb(n)g have zeromean and are independent of the jammers and thenoise. Note that when the Wiener weight vector isused in (10), the output d(n) contains the GPS signalof interest, AGN, and residual interference signals.

E. Decision Device

After the one-stage CM array, a signum-functiondetector is used to estimate the data because thetransmitted GPS signal is real and binary. Thus, thefinal output of the multicomponent receiver is givenby

b(n) = sgn[real[d(n)]]: (17)

F. Computational Complexity

The overall required number of multiplications/divisions and additions/subtractions for estimating aGPS data bit using the modified despreading systemis approximately NM +2M +2¼NM and (N ¡ 1)M+2M ¼NM, respectively. This computationalcomplexity is much lower than that of previouslyproposed systems as described in [16]. (Note that weignore the computational complexity of the decisiondevice.)

IV. INTERFERENCE REJECTION SYSTEM FORMULTIPLE GPS SIGNALS

In this section, we consider using the modifieddespreading approach to extract multiple GPSsignals. Fig. 2 shows the system architectureconsisting of the modified despreader for multipleGPS signals, a multistage CM array, and a binarydecision device. Using the modified despreader,all CM interference signals are transformed intonon-CM signals, while the GPS signals retain the CMcharacteristic. The output of the modified despreaderis processed by the multistage CM array to null all



Fig. 2. System architecture of modified despreader andmultistage CM array for multiple GPS signals.

jamming signals and sequentially extract the GPSsignals of interest.

A. Signal Model for Multiple GPS Signals

For multiple GPS signals, (1) can be rewritten as

x(k) =Ac

2664c1(k)b1(k)

...

cK(k)bK(k)

3775+ACMjCM(k) +AWBjWB(k) + v(k) (18)

where each column of Ac (size M £K where K is thenumber of satellites) is the AOA array response vectorfor a GPS satellite, and ci(k) and bi(k) (i= 1, : : : ,K)are the elements of the cyclostationary PRN code andGPS data bits, respectively, for a satellite.

B. Modified Despreader for Multiple GPS Signals

Assuming that the satellites labeled i= 1, : : : ,K aretransmitting the GPS signals of interest, the PRN codefor the ith satellite is given by

ci = [cai, : : : ,cai]T, i= 1, : : : ,K (19)

where cai is the C=A code (length-1023 row vector)for the ith satellite, which is repeated 20 times. In thissystem, a new PRN code ci is defined as

ci = [cai,1, : : : ,cai,20]T (20)

where cai,l (l = 1, : : : ,20) is randomly chosen from allpossible C=A codes, excluding cai (i= 1, : : : ,K). Formultiple satellites, we define the modified PRN code c

c¢=

KXi=1

ci¡ ci (21)

Fig. 3. Multistage CM array architecture for capturing multiple GPS signals.

and obtain the output of the modified despreader as

y(n) =Ac(N ¡ 20(K ¡ 1))

2664b1(k)

...

bK(k)

3775+ACMjCM(n) +AWBjWB+ v(n): (22)

Since only the term (N ¡ 20(K ¡ 1))[b1(k), : : : ,bK(k)]Thas the CM characteristic in (22), multiple GPSsignals can be recovered using the multistage CMarray [26].

C. Multistage CM Array

The multistage CM array shown in Fig. 3 operatessuch that the output of each stage contains a GPSsignal from a different satellite. A captured GPSsignal is removed by the signal canceler whose outputbecomes the input of the next stage. This processcontinues in a sequential manner until all GPS signalsare captured.The output of the ith CM array stage is

di(n) =wHi (n)yi(n) (23)

where yi(n) is the input signal vector (size M) (notethat y1(n)´ y(n)), and wi(n) is the adaptive weightvector updated by CMA as follows:

wi(n+1) =wi(n)+2¹yi(n)"¤i (n) (24)

with error signal

"i(n) = di(n)=jdi(n)j ¡di(n): (25)

The output of the CM array, which contains one GPSsignal, is applied to an adaptive signal canceler togenerate the following output vector:

yi+1(n) = yi(n)¡ fi(n)di(n) (26)

where fi(n) is the canceler weight vector (size M)updated by the LMS algorithm:

fi(n+1) = fi(n)+2¹LMSyi(n)d¤i (n) (27)

with step-size parameter ¹LMS > 0.



D. CM Array and Signal Canceler Wiener WeightVectors

Using the orthogonality principle (as was done inSection IIID) for the ith stage of the multistage CMarray, the beamformer Wiener weight vector is

wi,o = (N ¡ 20(K ¡ 1))2R+i ai (28)

where ai = Ti¡1£ ¢¢ ¢£T1ai, ai is the AOA vectorof the GPS signal captured in the ith stage, Ri =Ti¡1£ ¢¢ ¢£T1RyTH1 £¢¢ ¢£THi¡1, and

Ti¢=IM ¡ fi,owHi,o (29)

is a signal transfer matrix where IM is the size-Midentity matrix. Since the correlation matrix Ri issingular, we utilize the pseudoinverse R+i [27] in (28).The corresponding canceler Wiener weight vector is

fi,o = [(N ¡ 20(K ¡1))2=¾2si ]ai (30)

where ¾2si¢=wHi,oRiwi,o is the signal variance (power) at

the output of the ith stage.

E. Decision Device

For the ith satellite, the estimated GPS data bitsfrom the decision device can be written as

bi(n) = sign[real[di(n)]], i= 1, : : : ,K: (31)

F. Computational Complexity

Since this system requires only one despreaderand the multistage CM array, it is more efficient thanpreviously proposed systems for capturing multipleGPS signals. The number of multiplications/divisionsand additions/subtractions for estimating multipleGPS data bits at each sample instant n using themodified despreading system is approximatelyNM +4MK ¡ 2M +3K ¡ 1¼NM +(4M +3)K and(N ¡ 1)M +4MK ¡ 2M +K ¡ 1¼NM +4MK,respectively. For multiple GPS signals, thecomputational complexity of previously proposedsystems can be expressed as K £No where No isthe number of operations described in [16] for asystem with a single GPS signal. For example, for thegeneralized sidelobe canceler (GSC) considered in[16], which has the lowest computational complexityof those algorithms, there are NM(M +1)+KGSCMmultiplication/divisions and NM2 +KGSC(M ¡ 1)additions/subtractions, where we assume that theadaptive weights converge by the KGSCth sample.

V. COMPUTER SIMULATIONS

In this section, we present computer simulationexamples that demonstrate the performance of the

TABLE IFirst Scenario for Single GPS Signal

Signal Azimuth (±) Elevation (±) Center Frequency

GPS 67 ¡28 –CW 52,¡41 71, 71 0.05, 0.1FM ¡72,38 71, 71 0.17, 0.22WB 10,¡12 71, 71 0.33, 0.43

TABLE IISecond Scenario for Single GPS Signal


GPS ¡58 27 –CW ¡27 ¡62 0.35FM 41, 55 ¡62,¡62 0.20, 0.45WB ¡77 ¡62 0.1Pulsed 75,¡14 ¡62,¡62 –

modified despreading system for a single GPS signaland then multiple GPS signals using two scenarioswith M = 8 antenna array elements.

A. Computer Simulations for Single GPS Signal

For a single GPS signal, we consider two scenariosas described in Tables I and II. In the first scenario,the received signal consists of one GPS signal ofinterest, two CW jammers and two FM jammers asthe CM interference signals, two WB noise jammers,and AWGN. In the second scenario, the receivedsignal consists of one GPS signal of interest, one CWjammer and two FM jammers as the CM jammers,one WB noise jammer, two pulsed jammers, andAWGN. The two pulsed jamming signals are periodicon/off signals with periods of 100 and 1,000 samples.The SNR of the GPS signal is ¡30 dB, and thejammer-to-signal ratio (JSR) for each jammer is60 dB. In both scenarios, the FM jammers havemodulation index ¯ = 0:05 and normalized modulationfrequency fm = 0:001. For these simulations, wearbitrarily chose the center frequencies of the jammingsignals.Note that we cannot directly compare the

spectrum of the one-stage CM array output d(n) afterdespreading to that of the received signal x(k) becausethe time indexes are different. For simulation purposesonly, we reconfigured the multicomponent architecturesimilar to that in [16] where the modified despreaderfollows the CM array with weights copied from theoriginal adaptive configuration.Figs. 4 and 5 show the performance of the

modified despreader system by comparing thespectra of the received signal and the output of thereceiver for the first and second scenario, respectively.From these results, we observe that all jammers arealmost perfectly suppressed by the proposed systemfor a single GPS signal. The beampatterns of the



Fig. 4. Signal spectra for first scenario for single GPS signal.(a) Received signal. (b) Output of modified despreader.

Fig. 5. Signal spectra for second scenario for single GPS signal.(a) Received signal. (b) Output of modified despreader.

MVDR beamformer and the modified despreadersystem are shown in Figs. 6 and 7 for the firstscenario, and in Figs. 8 and 9 for the second scenario,which demonstrate that both systems have similarperformance regarding the null depth of the jammingsignals, even though the MVDR beamformer has fargreater complexity.

B. Computer Simulations for Multiple GPS Signals

For multiple GPS signals, we consider the twoscenarios described in Tables III and IV. In thefirst scenario, the received signal consists of fourGPS signals of interest, one CW jammer and oneFM jammer as the CM jamming signals, one WBnoise jammer, and AWGN. In the second scenario,the received signal consists of four GPS signalswith different SNR levels, one FM jammer as theCM jammer, one WB noise jammer, one pulsedjammer with an on/off period of 1,000 samples, andAWGN. The JSR for each jammer is 60 dB, andthe FM jammer has modulation index ¯ = 0:05 andnormalized modulation frequency fm = 0:001.

Fig. 6. Beampattern of MVDR beamformer for first scenario forsingle GPS signal.

Fig. 7. Beampattern of modified despreader system for firstscenario for single GPS signal.

Figs. 10 and 11 show the performance of themodified despreader system for SNR=¡28 dB bycomparing the spectra of the received signal and theoutput of the receiver. The results of the modifieddespreader system in the first stage of the CM arrayare shown in Figs. 10(b) and 11(b), respectively,for both scenarios. Observe that all interferers arealmost perfectly suppressed by the proposed system.Simulated signal-to-interference-plus-noise ratio(SINR) curves of the MVDR beamformer and themodified despreader system for multiple GPS signalsare shown in Figs. 12 and 13, respectively, forboth scenarios. Figs. 12(a)—(d) and Figs. 13(a)—(d)show the output SINRs for the first, second,third, and fourth GPS signals for both scenarios.They demonstrate that both systems have similarperformance (the maximum SINR difference of bothsystems is about 5 dB), even though the proposedreceiver is blind and has far less complexity than



Fig. 8. Beampattern of MVDR beamformer for second scenariofor single GPS signal.

Fig. 9. Beampattern of modified despreader system for secondscenario for single GPS signal.

TABLE IIIFirst Scenario for Multiple GPS Signals


GPS 12, 105, 167, 301 11, 11, 11, 81 –CW 57 47 0.25FM 136 47 0.4WB 251 47 0.1

TABLE IVSecond Scenario for Multiple GPS Signals


GPS ¡46,37,82,¡65 ¡14,77,¡14,¡14 –FM 67 ¡51 0.2WB 21 ¡51 0.35Pulsed ¡31 ¡51 –

Fig. 10. Signal spectra for first scenario for multiple GPSsignals. (a) Received signal. (b) Output of modified despreader

system.

Fig. 11. Signal spectra for second scenario for multiple GPSsignals. (a) Received signal. (b) Output of modified despreader

system.

the MVDR beamformer. The SINRs of the modifieddespreading system are slightly lower than those ofthe MVDR beamformer because the weights of themodified despreading system may not have convergedto the optimal values.

VI. CONCLUSION

GPS is particularly susceptible to high-powerinterference because it uses low-powerspread-spectrum signals. In order to overcomethis problem, we proposed an efficient and simpleblind multicomponent interference rejection systemconsisting of a novel modified despreader, followedby a one-stage CM array and the GPS decision device.The modified despreader transforms CM jammingsignals into non-CM signals so that a one-stage CMarray can null these interferers while extracting theGPS signal of interest, which has its CM characteristicpreserved. This system does not require explicit AOAestimation and it has a low computational complexity.



Fig. 12. Output SINR performance of modified despreadersystem and MVDR beamformer for multiple GPS signals for first

scenario. (a) First GPS signal. (b) Second GPS signal.(c) Third GPS signal. (d) Fourth GPS signal.

Fig. 13. Output SINR performance of modified despreadersystem and MVDR beamformer for multiple GPS signals with

different SNR levels (SNR1 = SNR2 +3 = SNR3 +7 =SNR4 +10 dB, where SNRi corresponds to ith GPS signal) forsecond scenario. (a) First GPS signal. (b) Second GPS signal.

(c) Third GPS signal. (d) Fourth GPS signal.

For capturing multiple GPS signals, we utilize amultistage CM array in place of the one-stage CMarray. Since this receiver requires only one despreaderfor multiple GPS signals, it is more efficient thanpreviously proposed systems. The performance of thesystem for interference suppression was illustratedvia example computer simulations and for a range ofjammer types.

REFERENCES

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Suk-seung Hwang was born in Seoul, Korea, on November 28, 1969. Hereceived the B.S. degree in control and instrumentation engineering fromKwangwoon University, Seoul, Korea, in 1997, and the M.S. degree in electricaland computer engineering from the University of California, Santa Barbara(UCSB), in 2001.Currently, he is working toward the Ph.D. degree in electrical and computer

engineering at UCSB, where, since September 1999, he has been employed as ateaching assistant or a graduate student researcher. His interests include adaptivesignal processing applied to wireless communications, interference cancellationfor GPS, and adaptive algorithms for an optical switch.Mr. Hwang was selected as Outstanding Teaching Assistant in Electrical

Engineering in 2002 and 2003.

John J. Shynk received the B.S. degree in systems engineering from BostonUniversity, Boston, MA, in 1979, the M.S. degree in electrical engineering and instatistics, and the Ph.D. degree in electrical engineering from Stanford University,Stanford, CA, in 1980, 1985, and 1987, respectively.From 1979 to 1982, he was a member of technical staff in the Data

Communications Performance Group at AT&T Bell Laboratories, Holmdel, NJ,where he formulated performance models for voiceband data communications.He was a research assistant from 1982 to 1986 in the Department of ElectricalEngineering at Stanford University where he worked on frequency-domainimplementations of adaptive IIR filter algorithms. From 1985 to 1986, he wasalso an instructor at Stanford University, teaching courses on digital signalprocessing and adaptive systems. Since 1987, he has been with the Departmentof Electrical and Computer Engineering at the University of California, SantaBarbara, where he is currently a professor. His research interests includeadaptive signal processing, adaptive beamforming, wireless communications, anddirection-of-arrival estimation.Dr. Shynk served as an editor for adaptive signal processing for the

International Journal of Adaptive Control and Signal Processing and an associateeditor for the IEEE Signal Processing Letters. Currently, he is an associate editorfor the IEEE Transactions on Signal Processing. He was technical program chairof the 1992 International Joint Conference on Neural Networks, and servedas a member of the IEEE Signal Processing for Communications TechnicalCommittee.



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