Practical Metropolitan-Scale Positioning for GSM Phones Presented by Khushnood Irani

Authors Mike Y. Chen Intel Research Seattle, USA Timothy Sohn University of California at San Diego, USA Dmitri Chmelev University of Washington, USA Dirk Haehnel Intel Research Seattle, USA Jeffrey Hightower Intel Research Seattle, USA Jeff Hughes University of Washington, USA Anthony LaMarca Intel Research Seattle, USA Fred Potter University of Washington, USA Ian Smith Intel Research Seattle, USA Alex Varshavsky University of Toronto, Canada

Need for Localization? The E911/E112 initiatives in the US & Europe specify requirements on Localization Accuracy for mobile phones placing emergency calls. These initiatives have catalyzed a market for network operator-provided location capabilities and services like: AT&T Wireless’ friend-finder. Sprint-NexTel’s fleet management tools.

Introduction To examine the location accuracy of a positioning system in a metropolitan environment we need to answer one question: Which platform serves best for location aware applications?

Introduction Global Positioning System (GPS) though the most common location technology, serves well for outdoor environments but provides inadequate information indoors. GSM & WiFi based location techniques are better in overcoming the shortcoming in GPS. Operators provide services to calculate mobile phone positions using: Hybrid Network Client Techniques Assisted GPS (AGPS) Network Only Techniques Enhanced Observed Time Difference(EOTD) Angle Of Arrival (AOA) Time Difference of arrival (TDOA)

Introduction WiFi beacon-based positioning system results in good indoor as well as outdoor location system with high coverage & good accuracy. Wide area beacon-based approach complement many indoor positioning systems that provide high precision in indoor environment but require specialized hardware or have high installation costs.

Mobile Phones: A Better Platform Introduction GSM is the most widespread cellular telephony standard in the world & has a subscriber base which is far greater than internet users. GSM mobile phones have a longer battery life, constant connectivity & are usually handheld. Range of GSM is 35km which is 70 times larger than the 500m range that WiFi offers. GSM network is well planned & stable as compared to the ad-hoc deployment of WiFi access points. GSM uses licensed frequency bands which is less prone to interference.

Methodology The Methodology is divided into three parts: Data Collection Description of the underlying hardware & software of the system as well as the type of data collected. 2. Trace Characteristics Description of the trace drive for the training & testing periods. Positioning Algorithms Examination & Comparison of the performance of the three position algorithms.

Data Collection Methodology Hardware 1. IBM Thinkpad T30 laptop (with a WiFi card) 2. Two GPS units 3. Three Sony Ericsson GM28 GSM modems 4. Three Audiovox SMT5600 phones (The phones & GSM modems contain SIM cards)

Data Collection Methodology Software The data collection Software is implemented in C#. This software records the Cell ID & the signal strength (in terms of dBm) for seven cells once every three seconds. Readings from the GPS are recorded every second.

Trace Characteristics Methodology Setup was implemented in a car with roof mounted antennas for better reception. Car driven through public accessible street in Seattle metropolitan area. For better accuracy the training data required to be large enough. Duration of the drive was 208 hours & covered 4350 km. 24 GB of traces collected which contain 6756 unique cells. The calibration trace was used to train three position algorithms.

Trace Characteristics Methodology To measure the accuracy of the algorithm, three test neighborhoods were selected for the testing phase of the analyses. (Downtown with high cell tower density & Residential with low cell tower density)

Trace characteristics Methodology Training Trace Testing Trace Name Downtown Residential Duration 208hr 70 min 169 min Distance 4350 km 24 km 89 km Dimension 25.0 km x 18.6 km 2.7 km x 2.3 km 2.6 km x 4.1 km + 4.6 km x 5.5 km Area Greater Seattle Downtown Seattle Ravenna East Bellevue Avg. Cell Density 28 cells/km2 66 cells/km2 26 cells/km2 Properties of the training & testing traces.

Positioning Algorithms Methodology The performance of three positioning algorithms are measured. Centroid Algorithm Radio Fingerprinting Algorithm Gaussian Process-based Monte Carlo Localization (These algorithms were chosen because they represent the spectrum of positioning algorithms & vary in complexity & accuracy)

Positioning Algorithms Methodology The algorithms were implemented in a C# location toolkit that runs on Microsoft Windows platforms such as: Smartphones PDAs PCs The toolkit can poll GSM readings & calculate the location four times per second using the centroid algorithm & the cell tower maps for the various cells observed.

Positioning Algorithms Methodology Analyzing the positioning accuracy of the algorithms was done in two phases: Training trace Time-stamped GSM & GPS measurements are used to build models specific to an algorithm. Testing trace GSM measurements are used to estimate the position & provide the estimated latitude & longitude values. (NOTE: The positioning error was computed by calculating the distance between the estimated position (GSM) & the true ground position (GPS))

Centroid Algorithm Methodology | Positioning Algorithms The centroid algorithm is very fast to compute & does not employ a radio propagation model. Using a lookup table (Cell ID, Latitude & Longitude), the algorithm estimates the phones location to be the geometric center of all the cells seen in the measurement. The algorithm also approximates the tower positions by averaging the places where the highest signal strengths for each was observed. (NOTE: In the USA, true cell tower locations are not publicly available.) To evaluate the placement accuracy, six cell towers were selected at random whose locations were physically verified. This method gave an average error of 56m & a maximum error of 76m. Weighting by the observed signal strength could improve the accuracy.

Estimated tower positions for the two test areas. Centroid Algorithm Methodology | Positioning Algorithms Estimated tower positions for the two test areas.

Radio Fingerprinting Algorithm Methodology | Positioning Algorithms The fingerprinting algorithm does not create a map of estimated tower positions, nor does it model radio propagation. In fact there is a radio profile that is feature rich in space and reasonably consistent with time. This method assumes that the radio beacons & the associated signal strengths observed at a location are stable over time. During the training phase the algorithm constructs a search index that maps the radio fingerprints to locations in terms of latitude & longitude coordinates. During the testing phase the algorithm uses the constructed search index to deduce the phone’s location by calculating the Euclidian distance in signal strength space between the current fingerprint & all available fingerprints in the index. The algorithm then selects K fingerprints with the smallest Euclidian distance & estimate the location of the device by averaging the latitude & longitude coordinates of the K matches.

Radio Fingerprinting Algorithm Methodology | Positioning Algorithms How does it work? The algorithm uses the K-nearest neighbors technique for matching fingerprints which estimates the location of the testing point in two stages. First, the algorithm scans through all training points and calculates the Euclidean distance in signal space between the testing point and each of the training points. The algorithm produces an estimate of the testing point’s location by averaging the locations of the K training points with the smallest Euclidean distance. To compute the Euclidean distance, the algorithm uses readings for all available radio sources in the fingerprint whose accuracy depends on the density of the collected fingerprints Example: if a training fingerprint contains signal-strength readings for 3 sources {Rtr1,Rtr2,Rtr3} and a testing fingerprint has signal-strength readings for the same 3 sources {Rtst1,Rtst2,Rtst3} then the Euclidean distance between the two fingerprints will be calculated as: (Rtr1 – Rtst1)2 + (Rtr2 – Rtst2)2 + (Rtr3 – Rtst3)2

Monte Carlo Localization Methodology | Positioning Algorithms This algorithm uses an abstract parametric radio propagation model with Markov localization to predict the location (estimated using a Bayesian particle filter). Gaussian processes are used to model the signal propagation that estimate the Gaussian distribution over functions based on training data. In order to achieve fast execution, the signal propagation function is pre-processed to a grid with 15m grid cells. The computation of the signal propagation can be implemented by a lookup function in the maps of the cell towers. The likelihood of an observation can be computed from the predicted signal strength provided the phone is at a particular location.

Monte Carlo Localization Methodology | Positioning Algorithms Standard Monte Carlo localization (called particle filtering) is applied to represent the posterior probability distribution about the positioning of the phone. In Monte Carlo Localization the phone’s position is represented by a set of random samples. Each sample consists of a state vector (which is the position of the device) & a weighting factor. The weight is the likelihood of the measurement at the particle’s location represented by the distribution of the samples & their importance factors.

Results The analyses presented explores the effects of the five factors on positioning accuracy. Choice of Algorithm Scan set size Simultaneous use of cells from different service providers. Training & testing on different devices. Calibrating drive density. NOTE: To characterize the GSM accuracy, the three service providers have been made anonymous. Also the results presented are based on HTC Typhoon phone used for testing & training purposes.

Effect of Algorithm Selection Results The positioning error was evaluated for each of the three algorithms using the test traces from the test areas. Downtown (Higher density) Residential (Lower density) 50% 90% Centroid 232m 574m 760m 2479m Fingerprinting 94m 291m 277m 984m Gaussian Processes 126m 358m 196m 552m Median & 90th – percentile positioning errors

Effect of Scan Set Size Results At any given time a GSM phone is in the range of a number of cells. The GSM phones & modems used provide not only information about the current cell with which they are associated but also six nearby cells thus making a total of seven cells. The number of cells are varied between one & seven by sorting them according to their signal strengths & using only the n-strongest cells.

Sensitivity analyses of positioning error versus the number of cells. Effect of Scan Set Size Results Sensitivity analyses of positioning error versus the number of cells.

Effects of Using Towers from Multiple Service Providers Results GSM devices only monitor the cells from towers of their respective service providers even though cell towers from other service providers may be closer & having stronger signals. Positioning accuracy ideally can be improved by increasing the number of observable cell towers thereby increasing the number of cells to scan. To evaluate this effect, a cross-provider device was simulated by combining the measurements from the three service providers.

Effects of Using Towers from Multiple Service Providers Results Downtown Residential Single Provider (7) Cross- Provider (7) Cross- Provider (All) Single Provider (7) Centroid 187m 166m 170m 647m 456m 574m Fingerprinting 94m 153m 245m 277m 313m 297m Gaussian Process 126m 87m 65m 196m 147m 134m Cross-provider median positioning error.

Effects of Using Towers from Multiple Service Providers Results Cumulative Distribution Function of positioning error for the Gaussian Process Algorithm in Downtown

Effects of Training on One Device & Testing on Another Results The algorithms require that another device should observe similar towers & its signal strength values must correlate with the HTC Typhoon phone. A transformation function with a strong correlation can convert the device’s signal strength values into those reported by the HTC Typhoon phone. Signal strengths of commonly observed cells were compared among the three GSM devices: A duplicate HTC Typhoon phone. An HTC Tornado phone. A Sony Ericsson GM28 modem. (These devices have different radio & antenna designs.)

Effects of Training on One Device & Testing on Another Results Devices Radio Antenna Average # of Common Cells Signal Strength Correlation Correlation Significance HTC Typhoon (reference) Same 7.000 1.000 .000 HTC Typhoon (duplicate) 6.484 0.828 < .001 HTC Tornado Different 5.018 0.789 Sony Ericsson GM28 Modem 4.283 0.874 Similarity between different GSM devices and the reference HTC Typhoon phone, showing the number of cells that are the common when two different devices scan at the same time. Pearson correlation coefficient and significance are shown for the signal strengths of these common cells between each device and the reference phone.

Effects of Training on One Device & Testing on Another Results Downtown Residential 50% % change Centroid 245m 5.6% 818m 7.6% Fingerprinting 366m 289% 803m 190% Gaussian Processes 206m 63% 307m 57% Cross-device median positioning error and % change when training with the trace collected on one device (the HTC Typhoon phones) and testing on another device (the Sony Ericsson GM28 modems)

Effects of Reducing Calibration Drive Density Results A tradeoff between the calibration drive density & positioning error is characterized by simulating sparser driving patterns from the comprehensive data set. Understanding this effect is useful to estimate the resource & cost necessary to calibrate a GSM-based positioning system. Sophisticated algorithms rely on calibration data to improve positioning accuracy because the radio models degrade in quality with less calibration. Vital street grid patterns are superimposed on the dense calibration trace & measurements that do not fall on the virtual street grid are filtered. To reduce the systematic error, five random offsets for each grid width are used which are then averaged to estimate the positioning error.

Effects of Reducing Calibration Drive Density Results Example of a generated, virtual street grid that simulates a drive density equivalent to 10% of the full training trace.

Effects of Reducing Calibration Drive Density Results Median positioning error as a function of the calibration drive density using simulated street grids for downtown.

Conclusion Summary of events: Examined the practical challenges in deploying a GSM beacon-based location system in a metropolitan environment. Characterized wide-area positioning accuracy for three classes of algorithms & investigated the effects of several practical issues such as cross-device positioning & calibration drive density. Presented a novel cross-provider positioning technique that improves positioning accuracy significantly.

Conclusion Storage Required CPU Usage Accuracy (Dense Towers) Accuracy (Sparse Towers) Required Density of Training Data Requires Same Device Training Set Benefits from Cross- Provider Scanning Tolerant of Phones Exposing Single Cell Centroid Low (445KB) Low 232m 760m No Yes Fingerprinting High (188MB) Medium 94m 277m High Gaussian Processes Medium (80MB) 126m 196m Summary of the characteristics of the three positioning algorithms.

Conclusion What the results show: Analysis of the calibration drive density show that 30% of the data set was sufficient to provide comparable positioning accuracy & suggested that 60hrs of driving can cover a metropolitan area. Cross-device positioning is possible with a little degradation in accuracy (for centroid algorithm) on devices with different radios & antennas. Scanning cells across all available service providers can improve the accuracy.

Thank You!