WLAN TOA Ranging and GPS Hybrid System for Indoor Navigation

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1 WLAN TOA Ranging and GPS Hybrid System for Indoor Navigation Bo Li and Kyle O Keefe Position Location And Navigation (PLAN) Group Department of Geomatics Engineering, The University of Calgary BIOGRAPHY Bo Li is an MSc student at the University of Calgary in Calgary, Alberta, Canada. He attained a BSc degree from the same department in 21. His research interests are GNSS navigation, indoor positioning, and wireless location with Wi-Fi. Kyle O Keefe is an Associate Professor of Geomatics Engineering at The University of Calgary. He has worked in positioning and navigation research since His major research interests are GNSS system simulation and assessment, space applications of GNSS, carrier phase positioning, and local and indoor positioning with ground based ranging systems. ABSTRACT Position errors for a GPS standalone solution can exceed 2 meters in indoor environments due to limited satellite availability, weak signals and multipath. Therefore hybrid positioning methods have been developed integrating other signals and sensors including pseudolites, ultrawideband, cell ID and WLAN fingerprinting with GPS to aid navigation in indoor environments. Service providers using such hybrid solutions claim location accuracies on the order of 1 meters in open sky and 5 meters indoors. This paper presents test results obtained by integrating software based 2.4 GHz WLAN Time-of-Arrival measurements with GPS. Reference trajectories were obtained using conventional surveying, where the kinematic trajectory was formed by moving between presurveyed points. Tests were conducted in a high multipath indoor environment with limited satellite availability but with ranging from multiple WLAN access points to the mobile user. Results show that ranges from several overlapping WLAN nodes combined with GPS pseudorange measurements can provide five meter or better position accuracy in these kinds of indoor environments. INTRODUCTION Mobile device users now expect ubiquitous positioning with wide coverage, availability, and good accuracy, reliability and integrity. This expectation has been met to some degree for outdoor positioning with GNSS. However, indoor positioning is still not satisfactory mainly due to the fact that GNSS signals are difficult to track in indoor environments. Developing methods for indoor pedestrian navigation with accuracies similar to standalone GNSS navigation outdoors is therefore a goal for the positioning and navigation community. While previous development of indoor navigation systems was limited by market demand and incomplete infrastructure, the increasing popularity of mobile handheld devices, the widespread deployment of wireless communications network infrastructure, and an increasing interest in location-aware services have enabled many new approaches to solve the indoor wireless location problem. GNSS-only approaches have focused on dealing with signal attenuation from buildings, multipath and signal blockages. Many researchers have focused on improving the signal availability of GNSS using high sensitivity receivers (Lachapelle 27). Some GNSS signals can be acquired and tracked indoors, however the actual positioning performance is still degraded by noise and multipath. GNSS/INS integration, particularly MEMS IMU integration is another promising candidate for indoor applications. However, the accelerometers and gyroscopes suffer from accumulated error or drift unless a source of updates is available. Research also has been done on combining GPS with other TOA sensors such as ultra-wideband transmitters and pseudolites. TOA-types methods are popular but the implementation usually requires timing synchronization or time differencing techniques, requiring additional or special hardware to derive the precise times of flight. Ultra-wideband (UWB) / Code-DGPS integration has been verified using UWB augmented DGPS measurements where a position solution can be maintained as the Mobile User travels between outdoors and indoors. (Chiu et al 28) However, the multipath effect is a major threat for many TOA based indoor ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 1/1

2 applications even for sensors that are relatively multipath resistant (like UWB) (Molisch 25). Moreover, the RF interference between these systems and GNSS receivers are potential concerns as well. This issue is particularly serious for sensors such as pseudolites, which operate in or near GNSS bands. Other RF positioning methods such as Angle-of-Arrival or Time-Difference-of-Arrival are expensive to integrate due to the requirement for specialized antennas or the cost of setting up monitoring infrastructure. Cellular-based methods including Cell ID/E911 are not accurate enough to satisfy indoor applications. Finally RFID integration methods require major infrastructure upgrades and are usually limited to small, specialized, service areas. Comparing to all the above, WLAN constitute the main infrastructure to be utilized for wireless location finding techniques. WLAN signals are defined by the IEEE standard, which is used to implement WLAN networks in the 2.4, 3.6 and 5.4 GHz frequency bands. The first goal of WLAN is to provide local wireless access to fixed network architectures. Its market is growing rapidly as the flexibility; connectivity, mobility, and low cost of this technology are welcomed by consumers. Therefore it is an excellent platform for building indoor positioning techniques. The main advantages of WLAN based indoor positioning systems are that it can be built on many existing infrastructures, it has great availability and its frequencies are limited to 2.4 GHz/5.4 GHz, which should not be interfere with GPS or other essential RF services operating below 2 GHz. In recent years, as WLAN-based location methods have become more popular, the most common approaches for WLAN based positioning have been database training oriented. One method is to use Received Signal Strength Index (RSSI) for fingerprinting location. Many researchers have shown that good accuracy can be achieved using fingerprinting techniques (Chan, S. and G. Sohn 212); however the database training process is time-consuming and changes in the environment will lead to degradation of the system and frequent database updates are needed. Another common method is combining WLAN location with a Radio Frequency Identification (RFID) tag (Ting et al. 211). This approach enables a device to record a scan of an RFID tag. In this manner, the mobile user can infer the location of RFID tags and transfer this information via the network. Automated inventory systems have widely adopted this method. The disadvantage is that a considerable cost is involved in implementing RFID systems. Another conventional way of using WLAN for ranges is based on a signal propagation model, which considers the signal strength as being inversely proportional to the square, or other path loss exponent, of distance. (Sandeep et al. 28) For example P R (db) = P T (d ) 1 log (d/d ) (1) expresses the simple relationship between Power Received and Power Transmitted in free space (where d is distance, α is the environment coefficient). However it has been found by many researchers that the propagation model is not sufficient to interpolate range for indoor navigation as it requires constant Line-of-Sight and a large a-priori database of environment coefficients associated with each access point. WLAN based TOA ranging research started in the late 199s. McCrady et al. (2) first proposed a TOA trilateration method based on only RF measurements, which can achieve a circular error probable of 2 meters (5% probability). Gunther and Hoene (25) obtained range estimation from TCP/IP packets; TOA estimation with help of additional timing hardware can provide a resolution of the measurement up to 22 nanoseconds level (Ciurana et al. 27). This would allow the user to achieve ranging accuracies of about 5 meters. However, their implementation required a costly high frequency clock (44MHz) and a long integration time that is not suitable for general pedestrian users. Depending on the protocol, an innovative TOA-based ranging technique over IEEE networks was proposed by Ciurana et al. (27). This approach is based on RTT (Round Trip Time) measurements using standard IEEE link layer frames. Ranging results obtained using the proposed technique show an encouraging achievable ranging precision with meter level errors. The approach described in by Ciurana et al. (27) shows that WLAN packets can be measured using today s inexpensive commercially available equipment to determine the distance between two wireless nodes for location sensing applications. The results of this method have shown ranging accuracy of several meters up to a 5 meter operational range when LOS is available. Moreover, a more recent software based approach proposed by Hoene and Willmann (28), unlike the other two-way TOA range estimation methods, shows that Round-Trip-Times of IEEE MAC packets sequences (e.g., RTS, CTS, DATA and ACK) can be used instead of traditional ping request packet pairs (e.g., DATA and ACK). This method is uses an indirect method to trilaterate between WLAN nodes by using a third node to monitor the transmission between the transmitter and the destination in order to estimate the range between them based on multiple observations. The advantage of this method is that it requires no redesign or update to existing WLAN device drivers and it can be applied easily with most existing WLAN hardware while providing user equivalent accuracy much better than finger printing or any propagation model WLAN location ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 2/1

3 algorithm. Trilateration positioning precision on the order of 4 meters was reported. Figure 1: Indoor four way TOA estimation (after Hoene and Willmann. 28) In addition to the WLAN TOA ranging algorithm developed by Hoene and Willmann (28), we propose a GNSS WLAN TOA hybrid method to preform simultaneous localization using range observables from WLAN TOA estimates integrated with GNSS pseudorange measurements using tight coupling in an Extended Kalman Filtering for 2D indoor / urban canyon environments. Furthermore, for multiple MU scenarios, this method can be further extended by using a network adjustment process to refine static positions of each user over a short period (eg. 3s). WLAN RANGING METHOD ADOPTED In 28, Ambisense Lab Group, Hoene and Willmann from University of Tubingen published their four-way TOA WLAN ranging method and released the algorithm as open source software: Goodtry (link). This algorithm estimates the RRT of the IEEE pockets as described in the previous section. packets sent in a sequence, where RTS and CTS are WLAN sequence modes: Request-to-Send and Clear-to- Send. Moreover, the SIFS is the Short-Inter-Frame-Space in between each mode. However, the TOF measurement cannot be estimated merely based on one measurement, it requires an average of many measurements from multiple observations to average over quantization error that results from using only the hardware time stamp measured when each packet is received. Additionally, internal delays denoted T ara must determine either via an Access Point node to node calibration or using the internal calibration function that the WLAN card provides automatically. Then the distance can be determined by: d i = ( T tof_i - T ara_i ) c / w i (3) Where w is the number of transmissions. Multiple distance observations are then averaged to achieve a more refined estimate. The WLAN ranging software Goodtry implemented for Linux-based systems due to the fact that it provides lowlevel access to WLAN device drivers. The ranging time stamps were taken from each WLAN card chipset. Additionally the IEEE control packets shown in Figure 1 (RTS, CTS, ACK ) must be provided. This information can be accessed on Atheros WLAN cards via the PCAP library included with most Linux distributions. Thus the Goodtry software can calculate the time of flight from the estimated transmission duration of the packets, for which the frequency, the modulation rates and packet lengths are known. The result is a list of all possible transmission times calculated due to the fact that the physical preamble durations are different on each IEEE standard. However they will be compared with the actual measured value and the closest estimated values will be recorded to form a set of samples that is then used to determine an average, variance, and confidence interval of the measurements. The Figure 4 shows a range estimate output of obtained using this software. Figure 3: An estimated distance from Goodtry Figure 2: Components of Goodtry TOA system (From Hoene and Willmann 28) T tof = (T 2D T 1D T CTS - T SIFS )/4 (2) As shown in equation 2 and Figure 1, the Time-of-Flight measurement can be obtained from monitoring multiple Assessment of the WLAN ranging accuracy was carried out prior to further integration with GPS measurements to analyze the operational range and accuracy of this method of WLAN ranging. Figure 4 shows a plot of WLAN ranges and range errors observed by a static user in optimal conditions (LOS). ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 3/1

4 Measured distance (m) Error (m) WLAN Ranges Assessment Actual distance WLAN Static Ranging Error Over Distance Actual Distance (m) Figure 4: WLAN ranges and range errors Even though the initial range estimate is somewhat biased, the range estimation is proportional to the actual distance. Moreover, little correlation is found between number of outliers and distance between the MU and AP. The WLAN ranges assessment shows that this method can provide ranges with an accuracy of 4 m over operational distances of up to 23 m in indoor environments. Given these results, coupling these measurements with GPS observations may be promising. LIMITATIONS Before conducting an experiment to integrate these ranges with GPS, we identified the following limitations that will likely influence the accuracy of WLAN ranging performance. Propagation Most published WLAN time-of-flight investigations assume the vacuum light speed, regardless of the real propagation environment. While the small differences between the vacuum speed and the propagation speed in air will be insignificant over the short distances involved, the effects of other materials (concrete, wood, plastics, fabrics, etc) have not been considered. The other main source of propagation errors is the presence of Non-Line-of-Sight conditions, which can result in an overestimation of the distance between AP and mobile user. During our indoor tests, the operational range was successfully tested up to about 25 meters and there was no unexplainable degradation in range availability or accuracy shown. The WLAN range measurement is known to be sensitive to fast user motion and blockages. Packet Sequence With increasing distance the WLAN transmission packet loss rate is expected to increase due attenuation. According to previous tests done by Hoene et al. (23), the 1 Mbits data rate is most suitable for ranging applications. It was found in this test when the distance was greater than 2 meters, the ranges output from the software estimation were not stable. This issue is likely caused by delayed or unresponsive pings. When tests were being conducted the network ping flood caused a heavy load over the occupied WLAN channels. Due to the four-way pocket sequence, the efficiency of transmission becomes lower. (Hoene and Willmann 28) This is a concern for any practical implementation of this kind of WLAN TOA ranging system. The next section describes our integration algorithm and then a series of tests to validate the method. INTEGRATION OF WLAN WITH GPS PSEUDO- RANGES GPS pseudoranges and WLAN ranges were integrated using an Extend Kalman Filter (EKF). In this case, the measurement vector consists of GPS pseudoranges and Doppler estimates from C/A code signals of all available satellites. Carrier phase measurements were not considered given the indoor application. The WLAN ranges were then added to the filter as direct ranges, assuming the access point coordinates were known and furthermore that the predicted value of the position state was close enough to true value for linearization. The variance of the WLAN measurements was assumed to be 4 meters based on the ranging assessment test previously conducted. ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 4/1

5 The user, assumed to be a slow moving pedestrian, was modeled as a random walk velocity process, where parameter of random walk is estimated to be about 1.2 m/s. The EKF-based filtering algorithm is commonly used for non-linear state estimation problems in positioning, localization, and navigation applications. It is an extension to the conventional Kaman Filtering method. (Brown and Hwang 213). The original position of MU is be estimated using a batch least squares adjustment of the first epoch of observations, The linearized discrete form of the state-space model describing the system is Prediction Stage: x = Φ x + k kk, + 1 k + T k =Φkk, + 1 kφ kk, + 1+ k P P Q The EKF will process all the observations gathered from all pseudoranges and available WLAN ranges. Given the above system and measurement model under the stated assumptions, the recursive discrete EKF update is: + x = x + K( z Hx ) T T K= PH ( HPH + R) (5) + P = ( I KH) P k k k k (4) included a mobile user travelling through a series of presurveyed control points, while recording measurements from a commercial GPS receiver and WLAN ranges using the Goodtry software. Some software updates were made in our implementation for this experiment including simplification of the test setup for better integration with an off-the-shelf commercial GPS device (Ublox ANTARIS 4) and recording WLAN data in PC time, to be synchronized with GPS time in post processing. The monitor node of the test setup is synchronized with mobile user in PC time. Final, the propagation velocity was changed to account for local temperature, air pressure, and humidity. The WLAN and GPS antennas were mounted on a pole attached to a media cart. The bottom end of the pole was pushed along the pre-surveyed trajectory marks (on the ground) travelling through the control points. Measurements from both systems where synchronized to GPS time. Two laptops with four Acheron AR5212 chipset cards and an Intel chipset card were used, each connected to an external 2.4 GHz WI-FI antenna. One laptop was set as the monitor station while the other acted as the mobile user. Four off-the-shelf wireless routers (Two DI-524, a DI-625 from D-Link and a Belkin N wireless router) were used in client mode to represent access points. They were mounted at the same height as the antennas on the mobile cart. The state vector is estimating position, velocity, GPS clock bias and drift. The only difference for the design matrix rows for the WLAN ranges is that they do not contain a clock term. A random walk velocity model was used in the filter. The process noises of.5m/s,.5 m/s and.1 m/s are assumed in East, North, and vertical respectively. EXPERIMENTAL VALIDATION In order to evaluate the WLAN/GPS integration as described above a test was conducted in the foyer of University of Calgary Olympic Oval, where about 1/5 of the roof consists of sky lights. This allows the user GPS receiver to track some GPS signals while the environment is still hostile. A multipath rich environment was selected to make GPS and WLAN observations and estimate the user position. WLAN ranges were measured from the user to access points located at four corners of the site. The AP layout is such that is should be sufficient to determine the user position in two-dimensions, via trilateration, when LOS ranges are available. The test Figure 5: Indoor Trajectory and Control Points (Illustration, not to scale) The access points and the mobile user trajectory are shown in Figure 5. The Mobile User started from one surveyed control point and took about 3 seconds to travel around the building and come back to the original spot; (MU was travelling from point 1 to point 14 and back to point 1). The media cart carrying the equipment was pushed along the pre-surveyed trajectory with the ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 5/1

6 MU occupying each check point for a period from 5 to 45 seconds Courtesy of Google earth Figure 7: Atheros AR5212/5213 chipset cards (Left) WLAN and GPS antennas on test pole (Right) Ublox GPS Receiver (Bottom) WLAN TOA Ranging Figure 6: The indoor test site surveyed by EDM As shown in Figure 6, the environment was a metal and concrete structured building, where the effect of multipath and signal attenuation should be significant on both WLAN and GPS signals.. On the pre-surveyed trajectory, the MU passes several metal covered ducts, highly reflective surfaces and concrete pillars, while the surrounding walls are generally smooth reinforced concrete. Continuous WLAN logged ranges Figure 8: Continuous WLAN Error over time In Figure 8 the measured WLAN ranges from the 4 AP nodes as logged by the software are shown in the upper plot. The value -1 represents a measurement Not Available. The bottom plot shows the range error with respect to the pre-surveyed reference trajectory. The average time between checkpoints is 1 seconds. Noticeably, the measurement error greatly increases after entering the main building from the glass tunnel near the starting ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 6/1

7 point. Figure 9: Histogram of WLAN Error Figure 9 is a histogram of the range errors. The majority fall between the ranges of -4 to 4 meters, which matches the expected accuracy of the method reported by Hoene and Willmann (28). STANDALONE GPS SOLUTION GPS data was used to compute a GPS only navigation solution. Figure 11: UTM GPS Standalone Indoor Error Figure 11 shows the easting and northing errors obtained from using only GPS observations in the filter. The solution is reasonably good for the first 1 seconds, while MU is under a glass ceiling link between two buildings with good visibility. Once MU enters the main building and the sky view is partially or entirely blocked at curtain points the solution degrades. The large spikes at 18s and 3s were times where the GPS availability was near zero. Figure 12: HDOP of Indoor GPS Figure 1: UBLOX 2D local level single point positioning trajectory (local estimation) Courtesy of Google earth The local coordinate system inside the test area included two exterior points that were GPS surveyed in order to transform between the local frame and WGS 84. Before the test MU cart was held static in a location with indoor open sky condition (6~8 satellites) for over 15 minutes to ensure a hot start for the GPS data collection. When the test started the MU travelled around the building and the sky view was partially or entirely blocked. The positioning output was impacted significantly as shown in Figure. Figure 12 shows the corresponding HDOP values during the test. Absolute Error (m) Percentile of WLAN and GPS Error Distribution GPS N GPS E WLAN Percentage % Figure 11: WLAN/ GPS Error Percentile ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 7/1

8 The WLAN ranging error and GPS North/East error percentiles are shown in Figure 11. Based on this the WLAN accuracy is compatible with performance of GPS in this indoor environment as expected. Thus the GPS/WLAN integrated position solution should offer an improvement over using either of the systems alone. The next step is to introduce WLAN measurement into the filter which should address the poor geometry. INTEGRATED SOLUTION UTM dnorth (m) Ublox indoor trajectory limited skyview GPS control Path Check Point APs Ublox trajectory Figure 14 shows local estimate of 2D position after the integration of WLAN ranges. The envelope of error is well within -5 to 5 meters most of time, and the large error spikes at 18s and 32s were completely eliminated UTM deast (m) Hybrid indoor trajectory limited skyview GPS control Path Check Point APs Hybrid trajectory 2 UTM dnorth (m) Figure 14: UTM WLAN Integrated Indoor Error However in the last 1 seconds of the test we can see increased 2D error, where the mean error value of the northing component is over 5 meters. This issue is likely caused by inference and blockage in the end of test, where the MU is leaving the building and entering the glass ceiling tunnel but has not yet reacquired good GPS coverage. The accuracy of WLAN measurements also declines significantly as shown by check points 3 to 35 in Figure 7. The trajectory of integration method is visually much improved over the GPS standalone method. The path of the travelled loop is clearly shown in the Figure UTM deast (m) Figure 15: Trajectory with/without WLAN integration Residual (m) Residual (m) GPS residuals after filtering process Hybrid residuals filtering process Figure 16: GPS residuals with/without WLAN integration Figure 16 shows the GPS residual values before and after adding the WLAN measurements. The divergences at epoch 13s and 3s were eliminated after integration which reduced subsequent innovation values due to the ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 8/1

9 predicted solution being closer to both the observations and the true solution. Also, notably, the problem observable 3 was able to be removed using innovation testing after applying the WLAN integration. 1999). The mean value of the re-computed variance of unit weight for each baselines and the a priori VCV matrices will be formed. The baselines are processed via a least squares adjustment procedure. The baselines and their a priori variance covariance matrices are treated as new observations in a minimally constrained adjustment: fixing at least one known point in this case. The fixed point would be the one AP that has the least estimated variance (e.g. direct LOS). Figure 17: Hybrid 2D RMS error histogram Over 6% of the position errors are within the range between 2.5 to 4.5 meters with 68% of the errors being less than 3.52 meters. The 2D accuracy is improved 32% after adding the WLAN measurements. Table 1: Error Statics Error GPS (m) Hybrid (m) North RMS East RMS D RMS Std. Dev When a pair of Mobile Users (MU) is positioning simultaneously, and each one takes static GPS measurements for some period, a DGPS baseline can be processed and overall accuracy for each MU should be improved after removal of any spatially correlated errors. Furthermore, we can apply the observed WLAN ranges to further improve the overall accuracy for the MU via network adjustment. A case for network adjustment between two MUs was demonstrated in the test. As shown in the table below the relative position errors with respect to the surveyed reference points were significantly improved after the additional WLAN ranges were added to the solution and the positions were reestimated while fixing one AP with known coordinates. The weighting on each WLAN range can be determined by the RSSI as a quality indicator. NETWORK ADJUSTMENT To extend the use of WLAN GPS integration, we considered a network scenario, in which, multiple users are managed within the same central system via WLAN. In this case, a network adjustment procedure can be carried out to refine the relative position within the network while preserving some fixed constraints. This procedure can be accomplished as follows: All possible combinations of pairs of mobile user must be processed as GNSS baseline solutions using WLAN observations if available. This process will help to detect the outliers of the measurements. The covariance matrices of the baselines will be scaled by the observation variance based on GPS DOPs and WLAN RSSI. The estimated variance of unit weight is re-computed by standardizing the covariance matrix with a correlation coefficient between corresponding observations. (Han and Rizos Figure 18: Network adjustment Demonstration Table 2 shows the results of two mobile users using GPS along, DGPS with respect to each other, and using a network solution (DGPS with all WLAN ranges between MUs and APs included). The mean WLAN range accuracy is also shown. ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 9/1

10 2D Local RMS Error CONCLUSION Table 2: Adjustment Statics The use of a simple TOA algorithm and static network adjustment method to combine indoor WLAN based TOA ranging with GPS pseudorange measurements has been demonstrated in this paper. The objective was to incorporate these two types of sensors to track an indoor pedestrian user. The Extended Kaman Filter employed provides good performance given the limits of the system. With GPS standalone the horizontal position accuracy with a limited sky view is about 6.8 meters. When integrating with WLAN range observables, the results improve to 4.6 meter accuracy which is a significant improvement over GPS alone. The result suggests that this approach could be employed to provide improved indoor GPS positioning. FUTURE WORK Further research is required to assess system performance when few to no GPS satellites are available. Different dynamics models and integration with other sensors should also be investigated. The Goodtry method should be tested on high transmission rate WLAN bands. ACKNOWLEDGMENTS Thanks Eric Fiselier from the University of Calgary Computer Science Department for kind help in the experiment setup and software debugging. REFERENCES 2D DGPS RMS Error Mean WIFI Range Error Network Adjustment Error (Fix 1 AP) MU m 3.18 m 3.4 m 1.93 m MU m 2.81 m 3.5 m 1.64 m Brown, R. and P.Y.C. Hwang. (213) Introduction to Random Signals and Applied Kalman Filtering: With MATLAB Exercises and Solutions. 4th edition. Wiley published ISBN Chan, S. and G. Sohn (212) Indoor localization using wi-fi based fingerprinting and trilateration techniques for LBS applications, 7th International Conference on 3D Geo-information 212, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-4/C26 Chiu, D., K. O Keefe (28) Seamless Outdoor-to- Indoor Pedestrian Navigation using GPS and UWB, Procedings of ION GNSS 28, Session F6, 12 pages. Ciurana, M., F. Barcelo-Arroyo, and F. Izquierdo, (27) A ranging system with IEEE data frames, in IEEE Radio and Wireless Symposium, 27, p Han S.W. and C.Rizos (1999), Network Adjustment Issues Using Mixed GPS Surveying Techniques, School of Geomatic Engineering University of New South Wales. Han, S.W. and C. Rizos (1995) Selection and scaling of simultaneous baselines for GPS network adjustment, or correct procedures for processing trivial baselines, Geomatics Research Australasia, 63, p Hoene, C., A. Günther, A. Wolisz (23) Measuring the Impact of Slow User Motion on Packet Loss and Delay over IEEE 82.11b Wireless Links, Local Computer Networks, 23. LCN '3. Proceedings. 28th Annual IEEE International Conference, p Hoene C., and J. Willmann (28), Four-way TOA and Software-Based Trilateration of IEEE Devices, Personal, Indoor and Mobile Radio Communications, (PIMRC 28). IEEE 19th International Symposium, p.1-6 Hoene. C., (28 June) Goodtry - software based WLAN trilateration.[online].available: ects/goodtry/ Lachapelle, G. (27) Pedestrian navigation with high sensitivity GPS receivers and MEMS, Personal and Ubiquitous Computing, Volume 11, issue 6, p McCrady, D., L. Doyle, H. Forstrom, T. Dempsey, and M. Martorana (2) Mobile ranging using low-accuracy clocks, IEEE Transactions on Microwave Theory and Techniques, vol. 48, no. 6, p Molisch, A.F. (25) Ultra wideband propagation channels-theory, measurement, and modeling, Vehicular Technology, IEEE Transactions on Volume 54, Issue 5, p Sandeep, A.R., Y. Shreyas, S. Shivam, (28) Wireless Network Visualization and Indoor Empirical Propagation Model for a Campus WI-FI Network, World Academy of Science, Engineering and Technology 28, Issue 18 p Ting, S.L., S.K. Kwok, A.H.C. Tsang and G.T.S. Ho (211) The Study on Using Passive RFID Tags for Indoor Positioning, International Journal of Engineering Business Management, Vol. 3, No. 1 p ION GNSS+ 213, Session E1, Nashville, TN, 16-2 September 213 Page 1/1

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