Presented at GNSS 2004

The 2004 International Symposium on GNSS/GPS

Sydney, Australia 6–8 December 2004

Differential algorithms for indoor wireless positioning system (DWPS)

Y. Wang X. Jia C. Rizos

Satellite Navigation and Positioning Group (SNAP) School of Surveying and Spatial Information Systems

The University of New South Wales (UNSW), Sydney, NSW 2052, Australia Tel: +61-2-9385-4206; Fax: +61-2-9313-7493; Email: Yufei@student.unsw.edu.au

Presenter Name(s)

ABSTRACT

The WPS is a 100% pure software based indoor positioning system, which uses the signal strength of WLAN (Wireless Local Area Networks) infrastructure, transmission from/to WLAN access points to determine the position of the user with mobile computing devices such as Personal Digital Assistants (PDA), laptop computer and so on. In the previous paper (Wang 2003), the authors give a detailed description of WPS system, including hardware, software and system architecture. Meanwhile based on the WPS test bed, several basic experiments were conducted such as: stability of 2.4GHz WLAN Infrastructure Radio Signal Strength, Reliability Experiment of the 2.4 GHz WLAN Infrastructure Radio Signal, Verification of the Empirical Model and the Effect of Geometry of Distribution (GOD), Wall Penetration Loss Experiment etc. From the results of the experiments and data analysis, it is concluded that a wireless access point-based indoor positioning system is feasible. Experimental results show a positioning accuracy of 1-3m. In order to improve the stability and reliability of WPS system, in this paper the authors propose two new methods, including differential algorithm, which is based on the popular differential theory, by setting up fixed differential correction base station in the same environment to compensate for the influence of susceptible radio frequency to rover station. In addition, from the analysis of positioning measurements, the authors find that, to some extent, signal strength of one fixed position is having a trend of weakness, which means that the signal strength value always weaken when it is affected by a different environment factor. So, based on this judgment, the author proposes a minimal signal strength value algorithm. KEYWORDS: Wireless positioning, Wireless Local Area Network (LAN) infrastructure, Signal propagation, indoors positioning.

1. INTRODUCTION Indoors Wireless Positioning System (WPS) or Wireless Access Point based indoor positioning system is becoming more and more popular with the availability of the IEEE 802.11 standard and relative low cost equipments, which conform to it (Seidel 1992, Aguirre 1994, Small 2000, Bahl 2003, Zhou 2003). For example since the first paper on an indoor wireless positioning system (WPS) based on wireless local area network infrastructure (Wang 2003), more than ten questions have been received and asked from France, Germany, Korea, Malaysia, Portugal, Singapore, Spain, Thailand, and USA to talk about this system. A lot of enquiries such as what is the core of the development of WPS? What is the architecture of WPS system? How about the accuracy of indoor positioning? Is it possible to introduce WPS into the location-based system? Someone even asked what analysis tools are used in processing the collected data. In particular, PEDAGOG, a consultant company of US army training facilities, has even tried to put WPS system into practice, which uses WPS for training purposes at Fort Polk in last November 2003. In addition, the leader of VEST, a virtual environment project of Escola Superior de Tecnologia de Setúbal and an integrated system of 3D model virtual reality, community interaction, telecommunication and positioning system etc, also expressed great interest to transplant WPS into VEST. So all these useful information and discussions encouraged us to do more, and think more about the “babyish” and “delicate” WPS system. So, in order to improve the stability and reliability of the WPS, in this paper we introduce the popular differential method into WPS system, which is based on differential theory, by setting up fixed differential correction base station in the same environment to compensate for the influence of susceptible RF to rover station. In addition, through the analysis of measurement of the signal strength, it is found that the signal strength of one fixed position is having a trend of weakness. So based on this judgment, we propose minimal signal strength value algorithm as well. This paper is organised as follows: Section 2 reviews the previous work and basic experiments. Section 3 describes the differential theory and the algorithm. Section 4 presents differential experiments and data analysis while section 5 introduces the minimal signal strength value algorithm. The section 6 concludes this paper. 2. PREVIOUS WORK AND BASIC EXPERIMENTS 2.1 Experimental Test Bed, WLAN Infrastructure and Hardware Before introducing new algorithms in detail, we want to summarize what we had done in the deployment of our WPS system. First, a test bed was established on the top floor of the 6-storey Electrical Engineering Building, in the main working zone of SNAP group at UNSW. The layout of this floor is shown in Figure 1. It has dimensions of 17.5m by 84m with about 40 different rooms, including classrooms, computer labs, offices, storerooms, tearoom and two long corridors.

17.5 m

AP2 AP4 AP6

AP3 AP1

AP5

84 m

Figure 1. Test bed for the SNAP Wireless Positioning System (WPS) with location of WLAN access points (AP)

Second, six WX-1590 SparkLAN 11 Mbps WLAN Wireless Multi-Mode Access Points (AP), as the wireless signal transmitters and base stations, were installed at the locations indicated with red stars in Figure 1. The rovers are an Acer eXtensa 710T laptop computer (Windows 2000 operating system) and Compaq iPAQ 3970 (Pocket PC 2002 operating system), with Lucent Technology Wi-Fi Orinoco Wireless Golden Card (Figure 2). These wireless network cards can detect and synchronize the signal strength (SS) from the six wireless Access Points. The 802.11 b (‘WiFi’) Telecommunication Protocol is used in this system.

Access Point Wireless Card iPAQ 3970

Figure 2. WPS hardware 2.2 Software implementation Generally speaking, the low-level or core of WPS can be taken as how to develop a hardware driver for Orinoco gold wireless card interface, and then based on this, a series of signal detecting algorithm, positioning algorithm, coordinate transformation algorithm and telecommunication algorithm also need to be provided, but all of these are located in the application layer. So here we want to give a particular description on WPS core architecture and application software architecture separately. 2.2.1 Core Architecture As described above, the core part of WPS system is how to write a wireless network card driver, for Windows operating system, there is 3-layer driver architecture (See figure 3) Moreover, every device is serviced by a chain of drivers typically called as a driver stack. Each driver in the stack isolates some hardware-dependent features from the drivers above it.

WIN32 API

Protocol Driver

NDIS

Hardware

Hardware Bus Driver Port and Mini-Port Driver

Class and Mini-class Driver Kernel-mode Client Driver

User-mode Client Driver

Application User layer

Kernel layer

Hardware layer

Figure 3: Core Architecture of WPS

Unfortunately, in the commercial Windows operating system, we cannot find the required functionality to extract the MAC address of the AP, Signal Strength (SS) information, Noise, Signal-To-Noise Ratio (SNR), transmitter channel of AP, basic service set identifier (BSSID), etc from wireless network card. So it is necessary to extend the Windows’s Network Device interface Specification or NDIS to provide user applications with the ability of accessing particular information from wireless network card. Therefore, in real-time implementation, we adopted the DeviceIoControl, the WIN32 API provided by Windows OS, to detect the NDIS hardware sensor and read the feature values in NDIS wireless hardware storage. NDIS describes the interface by which one or more Network Interface Card (NIC) drivers communicate with one or more underlying network interface cards, overlying protocol drivers, and the operating system. NDIS also defines a fully abstracted environment for NIC driver development. For every external function that a NIC driver needs to perform, it can rely on NDIS routines to perform the operation. This includes the entire range of tasks performed by a NIC driver, from communicating with protocol drivers to registering and intercepting NIC hardware interrupts, and communicating with underlying NICs by manipulating registers, port I/O, and so forth. Therefore, NIC drivers can be written entirely in platform-independent high-level languages such as C. These drivers can then be recompiled with a system-compatible compiler to run in any NDIS environment. NDIS on Windows 2000 To provide abstraction and portability at the level described above, Windows 2000 gives an NDIS export library referred here as the NDIS library or NDIS. All interactions between the NIC driver and protocol drivers, NIC driver and the operating system, and NIC driver and the network adapters that it controls, are through calls to NDIS functions. NDIS is packaged in an export library as a set of functions, with emphasis on in-line macros for maximum performance. All NDIS drivers, including highest-level NDIS protocol drivers, intermediate NDIS drivers and NIC drivers link with this library. When called, a NDIS function calls an associated function in a higher-level driver, an intermediate NDIS driver, a NIC driver, the operating system, or else performs an internal-to-NDIS local action. NDIS on Windows CE The NDIS implementation on Windows CE 3.0 is a subset of the NDIS 4.0 implementation used on Windows 2000. The complete NDIS specification supports several types of network

drivers, but Windows CE version 2.0 and later only support writing NDIS Mini-port drivers, not monolithic or full Network Interface Card (NIC) drivers. For NDIS Mini-port drivers, Windows CE is a source code compatible with Windows 2000, supporting identical NDIS APIs barring a few exceptions. Fortunately, NDIS 5 will support all of 802.11 wireless object identifiers (OIDs) in CE’s next version, Windows CE.NET. So, at that time, it will become easier to extract the needed information directly from wireless network card. 2.2.2 Application Software Architecture Apart from the extension of the functionality in NDIS, the authors have developed a complete indoors SNAP-WPS software package as well, including roving client side software for the iPAQ 3970 and Acer Laptop computer, and indoor tracking-monitoring program on the server side. The SNAP-WPS laptop version software was developed using Borland Delphi 7. The iPAQ version was developed using Embedded Visual C++ 3.0. In these experiments, the laptop was used as the roving client. Figure 5 shows the graphical user interface (GUI) of the application for the mobile client. According to the system demands, and following the principle of Internet software, a three-tier design was implemented to demonstrate this WPS system (Figure 4): (1) wireless positioning and tracking client side, (2) tracking and monitoring server side and, (3) remote monitoring client side.

Access Point

Access Point

Wireless Signal processing Component WPS Rover-side Software package Telecommunication

Component

WPS Remote Client Component

Wireless/Internet

WPS Tracking & monitoring

Server side

Client side displayer Component

Wireless Positioning Component

Access Point

Figure 4. WPS Application Software Architecture

Figure 5. Mobile client’s GUI interface for the laptop computer

3. THE DIFFERENTIAL THOERY 3.1 The propagation error of indoor wireless wave

In idealized environment, if wireless radio waves are transmitted from a point, they spread and propagate as spherical wave fronts. The wave fronts travel in a direction perpendicular to the wave front, as shown in Figure 6.

Figure 6. Radio wave propagation

However, in reality, whether indoors or outdoors, the mechanisms behind radio wave propagation are diverse and generally be attributed to reflection, diffraction, and scattering (Blake 1986, Durgin 1998). At the same time, in the indoors environment, conditions are much more variable as the distance covered is much smaller, and the variability of the environment is much greater for such a smaller range of transmitter and receiver separation distances. For example, the transmitted signal generally reaches the receiver via multiple paths; so multi-path causes fluctuations in the received signal envelope and phase, and the signal components arriving from indirect and direct paths combine to produce a distorted version of the transmitted signal. Multi-path within buildings is strongly influenced by the layout of the building, the construction materials, and the building type. Some artificial factor such as whether interior doors are open or closed inside the building; where antennas are mounted, how many people in the building and so on, also influence multi-path. Based on this consideration, a differential method is proposed to eliminate the errors that are common to the receiver environment.

3.2 The differential concept

The differential method is very popular and has been applied to many navigation and positioning systems. It relies on the assumption, that certain types of errors, which can degrade the performance and accuracy of a system, are common to all system components. If these errors can be calculated at a point, or those errors linearly correlated across different datasets can be eliminated by differencing (D.B.Grant et. al 1990). Their application to the data of the component, as a correction, will cause them to be removed or reduced. More succinctly, differential method involves the removal of correlated systematic errors between a reference and roving components. This, of course, is a surveying jargon (Kennedy 1996).

Obviously, the main assumption behind differential techniques is that they improve the overall system performance. To be able to define errors in any navigation system, the correct value of the observation either must be known or be calculable. For example, the errors inherent in GPS are visible to the user only as an error in the position or position uncertainty. These can only be quantified if the user actually knows where he really is. Prior knowledge of the position will allow these errors to be identified and also allow them to be defined and possibly mitigated.

It is therefore apparent, in a differential system at least one receiver or reference station must know where it is, i.e. at a known reference point. This obviously has an immediate and major cost significance.

3.3 System design of Differential WPS

Based on the basic differential concept, we designed a differential WPS system. See figure 7. In contrast with previous WPS, in DWPS system, we introduce a critical component - desktop computer with wireless card, the same software and hardware configuration used in the mobile receiver. This desktop computer, receiving the real-time signal strength from the same access point, is called a reference station.

Access Point 1

Access Point 2

Access Point 3

Figure 7. System design of differential WPS system

Mobile Wireless

Receiver

Desktop Receiver &

Reference Station

R3 ref R3

R2

R1 ref R2 ref

∆R1 ∆R2 ∆R3

R1

Naturally, the position of this reference station should be known. But here we don’t care about the true position, and the focus is the real time signal strength. Accordingly, the differential formula to one access point is shown below:

meandesktopobsdesktopdesktop SSSSSS −− −=∆ ⑴

desktopobslaptoplaptop SSSSDSS ∆−= − ⑵ Where: ∆ is real time differential correction value of desktop or reference station. desktopSS

obsdesktopSS − is real time SS observation value of reference station.

meandesktopSS − is the known signal strength of this position relating to a fixed access point.

laptopDSS is the applied measurements after differential correction between mobile and reference station

obslaptopSS − is the real time observation value of mobile station

4. DIFFERENTIAL EXPERIMENTS AND DATA ANALYSIS

In order to test the validity of differential method in the WPS system, a series of experiments including zero-baseline experiment, short-range, medium-range and long-range experiments have been carried out and the corresponding results are presented in the following sub-sections. 4.1 zero-baseline experiment

First, we conducted a simple but important zero-baseline wireless signal strength experiment to evaluate the effectiveness of the differential WPS algorithm, According to the differential theory, there is an assumption that certain types of errors, which can degrade the performance and accuracy of a system, are common to all system components. This is the essential term to apply to a differential algorithm. On the other hand, from zero-baseline experiment, we also can test the consistency of hardware, such as the desktop computer with laptop computer, the identicalness of two different wireless network cards, because this is the hardware prerequisite to apply the differential algorithm in positioning system. Therefore in practice, we put the laptop computer and desktop computer together and make them as close as possible, at the same time face to one access point as in figure 8. The laptop and desktop equipped with same software and hardware configuration are put together and they receive the signal from the same access point. After a long time of data collection, we got 7013 records in 24 hrs.

Access Point

Figure 8. Zero-baseline test of two wireless signal strength receivers

Figure 9. Zero-baseline test result

Figure 10. Zero-baseline differential result

Mean (dBm) Std. Deviation Desktop Station 60.84 2.05 Laptop Station 60.91 1.78

Laptop Station after D Correction 60.91 1.15

Table 1. The statistic result of Zero-baseline experiment From Zero-baseline test result (See figure 9), different test result (See figure 10) and differential statistic result (See Table 1), it is shown that with the equations (1) and (2), more stable signal strength is observable after the differential process. The standard deviation has improved significantly from 1.78 to 1.15, which means the environment relativity is very tight under the zero-baseline condition. So, as suggested above, this forms the basis for introduction of differential method into indoor wireless positioning system. In addition, this also verifies the consistency of hardware equipment. 4.2 short-baseline (up to 5 meters) experiment In the short-range (up to 5 meters) experiment, a laptop computer and desktop computer were located at two different positions and the distance between them is 5 meters. After a long time of collection, 12500 records were collected respectively for the two computers. The results are presented figure 11, 12 and table 2

Figure 11. Short-range-baseline up to 5-meter test result

Figure 12. Short-range-baseline up to 5-meter differential result

Mean (dBm) Std. Deviation

Desktop Station 51.64 1.79 Laptop Station 46.31 1.92

Laptop Station after D Correction 46.31 1.42

Table 2. The statistic result of short-baseline experiment

From short-baseline of 5-meter test result (See figure 11), differential test result (see figure 12) and differential statistic results (Table 2), it can be concluded that differential method is very successful, because after differential corrections the signal strength standard deviation of the mobile receiver has reduced from 1.92 to 1.42, which means, in this situation, the changes in the environment are reflected in the common errors and removed dramatically.. 4. 3 Medium-baseline (up to 10 meters) experiment In the medium-baseline (up to 10 meter) experiment, the distance between the laptop computer and desktop computer has been extended to 10 meters. After many hours collection, 2500 records were obtained for each computer and the results are presented in figure 13, 14 and table 3.

Figure 13. Medium-baseline up to 10-meter test result

Figure 14. Middle-range-baseline up to 10-meter differential result

Mean (dBm) Std. Deviation Desktop Station 51.66 1.66 Laptop Station 62.67 2.57

Laptop Station after D Correction 62.67 2.48

Table 3. The statistic result of middle-baseline experiment

From medium-baseline of 10-meters test results (See figure 13), differential test result (see figure 14) and differential statistic results (Table 3), it is found that differential method shows less correlation across different datasets and therefore less improvement in standard deviation to some extent, because after differential correction the signal strength standard deviation of mobile receiver only changed from 2.57 to 2.48, almost the same, which means the environment relativity of two station tend to become weak for a distance of 10 meters. This also may be explained as a marginal area of two environments selected for the two receivers. 4. 4 long-baseline (up to 15 meters) experiment In the same way, we extend the distance between laptop computer and desktop computer to 15 meters, and we collected 3600 records for each computer. The results are presented in figure 15, 16 and table 4.

Figure 15. Long-baseline up to 15-meter test result

Figure 16. Long-range-baseline up to 15-meter differential result

Mean (dBm) Std. Deviation

Desktop Station 57.66 1.33 Laptop Station 55.70 0.87

Laptop Station after D Correction 55.70 1.66

Table 4. The statistic results of long-baseline experiment In long-baseline up to 15 meters experiment, the differential statistic result (Table 4) has totally failed because after differential correction the standard deviation of mobile receiver didn’t reduce, on the contrary, it increased. Of course, this result happened by accident, but in some sense, it really proved that the two receivers are located in two completely different environments and the absence of correlation across the two sets of collected data.. 5. SIGNAL STRENGTH MINIMAL VALUE ALGORITHM There is no doubt that the true value of signal strength will provide accurate position. However, in real time and practical environment, the true values are not available for the most of the time. From the static experiment, it is found that signal strength is likely to be weakened by non-ideal environment. That means some difficult environment will easily cause signal attenuation. Figure 17 showed the likelihood of signal attenuation. It is believed that the strongest signal strength at one position will closer to the true value of that point.

Figure 17. Static experiment of signal strength

So based on this reasoning, a signal strength minimal value (or strongest value of signal strength) algorithm is proposed. In practice we use windows method to select the minimal value, namely a group of signal strength values at the different positioning waypoint is collected and, then the minimal value (or strongest value) of signal strength is used as the true signal strength value of that point in the positioning. For example, at a positioning waypoint, we use 1Hz-sampling rate to collect signal strength for 5 seconds to get 5 values, and then use windows method to choose the minimal value as the applied measurements value for that point. This method is presented in figure 18.

Figure 18. Windows method to select the minimal value every 5 records (one waypoint).

6. CONCLUDING REMARKS By use of the differential WPS, from the results of the experiments, it can be concluded that by using fixed wireless radio base station in the same environment as that of access point, remedies the diversification of wireless radio propagations such as reflection, diffraction, and scattering of radio frequency waves of different and complicated environmental factors. Especially, in the short (up to 5 meters) and medium (up to 10 meters) baseline experiments,

the correlation between rover station and fixed base station is much shorter and therefore, the differential corrections can improve the stability and continuity of wireless RF measurements greatly in these two cases. However, if the distance is longer than 15 meters, the measurements between rover and base station are totally dissimilar rendering the differential algorithms ineffective. Signal strength minimal value algorithm is based on a group of measurements in static experiment; the characteristic of leaping upwards makes it meaningful. In practice, we use Windows method to choose the minimal signal strength in a special short period as the measurement. Consequently, it can eliminate the disturbance factor as well. As a final note, differential and signal strength minimal value algorithm are two entirely different algorithms, but both focussed at removing the influence of outside effects. So, in practice, we can mix these two algorithms with the empirical model to get a robust real-time indoor position.

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