Suining He

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of Global Positioning System (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

Fusing noisy fingerprints with distance bounds for indoor localization

Fusing fingerprints with mutual distance information potentially improves indoor localization accuracy. Such distance information may be spatial (e.g., via inter-node measurement) or temporal (e.g., via dead reckoning). Previous approaches on distance fusion often require exact distance measurement, assume the knowledge of distance distribution, or apply narrowly to some specific sensing technology or scenario. Due to random signal fluctuation, wireless fingerprints are inherently noisy and distance cannot be exactly measured. We hence propose Wi-Dist, a highly accurate indoor localization framework fusing noisy fingerprints with uncertain mutual distances (given by their bounds). Wi-Dist is a generic framework applicable to a wide range of sensors (peer-assisted, INS, etc.) and wireless fingerprints (Wi-Fi, RFID, CSI, etc.). It achieves low errors by a convex-optimization formulation which jointly considers distance bounds and only the first two moments of measured fingerprint signals. We implement Wi-Dist, and conduct extensive simulation and experimental studies based on Wi-Fi in our international airport and university campus. Our results show that Wi-Dist achieves significantly better accuracy than other state-of-the-art schemes (often by more than 40%).

Sectjunction: Wi-Fi indoor localization based on junction of signal sectors

In Wi-Fi fingerprint localization, a target sends its measured Received Signal Strength Indicator (RSSI) of access points (APs) to a server for its position estimation. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in database. Due to signal measurement uncertainty, this matching process often leads to a geographically dispersed set of reference points, resulting in unsatisfactory estimation accuracy. We propose a novel, efficient and highly accurate localization scheme termed Sectjunction which does not lead to a dispersed set of neighbors. For each selected AP, Sectjunction sectorizes its coverage area according to discrete signal levels, hence achieving robustness against measurement uncertainty. Based on the received AP RSSI, the target can then be mapped to the sector where it is likely to be. To further enhance its computational efficiency, Sectjunction partitions the site into multiple area clusters to narrow the search space. Through convex optimization, the target is localized based on the cluster and the junction of the sectors it is within. We have implemented Sectjunction, and our extensive experiments show that it significantly outperforms recent schemes with much lower estimation error.

Contour-based Trilateration for Indoor Fingerprinting Localization

Trilateration has been widely and successfully employed to locate outdoor mobile devices due to its accuracy. However, it cannot be directly applied for indoor localization due to issues such as non-line-of-sight measurement and multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the choice of similarity metric between signal vectors. We propose INTRI, a novel, simple and effective indoor localization technique combining the strengths of trilateration and fingerprinting. For a signal level received from an access point (AP) by the target, INTRI first forms a contour consisting of all the reference points (RPs) of the same signal level for that AP, taking into account the signal noise. The target is hence at the juncture of all the contours. With an optimization formulation following the spirit of trilateration, it then finds the target location by minimizing the distance between the position and all the contours. INTRI further uses an online algorithm based on signal correlation to efficiently calibrate heterogeneous mobile devices to achieve higher accuracy. We have implemented INTRI, and our extensive simulation and experiments in an international airport, a shopping mall and our university campus show that it outperforms recent schemes with much lower location error (often by more than 20%).

Calibration-free fusion of step counter and wireless fingerprints for indoor localization

In order to improve the accuracy of fingerprint-based localization, one may fuse step counter measurement with location estimation. Previous works on this often require a pre-calibrating the step counter with training sequence or explicit user input, which is inconvenient for practical deployment. Some assume conditional independence on successive sensor readings, which achieves unsatisfactory accuracy in complex and noisy environment. Some other works need a calibration process for RSSI measurement consistency if different devices are used for offline fingerprint collection and online location query. We propose SLAC, a fingerprint positioning framework which simultaneously localizes the target and calibrates the system. SLAC is calibration-free, and works transparently for heterogeneous devices and users. It is based on a novel formulation embedded with a specialized particle filter, where location estimations, wireless signals and user motion are jointly optimized with resultant consistent and correct model parameters. Extensive experimental trials at HKUST campus and Hong Kong International Airport further confirm that SLAC accommodates device heterogeneity, and achieves significantly lower errors compared with other state-of-the-art algorithms.

Tilejunction: Mitigating Signal Noise for Fingerprint-Based Indoor Localization

In indoor localization based on Wi-Fi fingerprinting, a target sends its received signal strength indicator (RSSI) of access points (APs) to a server to estimate its position. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in the database. Due to signal noise in fingerprint collection and target measurement, this often results in a geographically disperse set of reference points (RPs), leading to unsatisfactory estimation accuracy. To mitigate the noise problem, we propose a novel, efficient, and highly accurate localization scheme termed Tilejunction. Based on only the first two moments of the measured signal, Tilejunction maps the target RSSI of each AP to a convex hull termed signal “tile” where the target is likely within. Using a novel comparison metric for random signals, we formulate a linear programming (LP) problem to localize the target at the junction of the tiles. To further improve its computational efficiency, Tilejunction employs an information-theoretic measure to keep only those APs whose signals show sufficient differentiation in the site. It also partitions the site into multiple clusters to substantially reduce the search space in the LP optimization. We have implemented Tilejunction. Our extensive simulation and experimental measurements show that it outperforms other recent state-of-the-art approaches (e.g. RADAR, KL-divergence, etc.) with significantly lower localization error (often by more than 30 percent).

Indoor Localization and Automatic Fingerprint Update with Altered AP Signals

Wi-Fi fingerprinting has been extensively studied for indoor localization due to its deployability under pervasive indoor WLAN. As the signals from access points (APs) may change due to, for example, AP movement or power adjustment, the traditional approach is to conduct site survey regularly in order to maintain localization accuracy, which is costly and time-consuming. Here, we study how to accurately locate a target and automatically update fingerprints in the presence of altered AP signals (or simply, “altered APs”). We propose Localization with Altered APs and Fingerprint Updating (LAAFU) system, employing implicit crowdsourced signals for fingerprint update and survey reduction. Using novel subset sampling, LAAFU identifies any altered APs and filter them out before a location decision is made, hence maintaining localization accuracy under altered AP signals. With client locations anywhere in the region, fingerprint signals can be adaptively and transparently updated using non-parametric Gaussian process regression. We have conducted extensive experiments in our campus hall, an international airport, and a premium shopping mall. Compared with traditional weighted nearest neighbors and probabilistic algorithms, results show that LAAFU is robust against altered APs, achieving 20 percent localization error reduction with the fingerprints adaptive to environmental signal changes.

Towards area classification for large-scale fingerprint-based system

In spacious and multi-area buildings, fingerprint-based localization often suffers from expensive location search. Besides, context knowledge like inside/outside-region and floor area is important for complete location service. To address above issues, beyond the algorithms finding the exact location point, we study accurate and efficient indoor area classification for large-scale fingerprint-based system. We first study leveraging the one-class classification to conduct inside/outside-region detection given only the inside fingerprints. Then we discuss different area determination algorithms, and compare their detection accuracy and deployment efficiency. To further enhance accuracy, we also discuss rejecting unclassifiable signals and calibrating heterogeneous devices. We have implemented different algorithms on Android platforms. Experimental trials (totally over 30,000 fingerprints and 15,000 test data) at an international airport, a business building, a premium shopping mall and a university campus have evaluated practicability and deployability of different classification schemes. Our studies can also serve as design guidelines for area classification.

INTRI: Contour-Based Trilateration for Indoor Fingerprint-Based Localization

Due to its accuracy, trilateration has been widely deployed to locate smartphones outdoors. However, such approach cannot be easily applied indoors due to issues like non-line-of-sight measurement and complex multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the similarity metric comparing signal vectors. We propose INTRI, a novel, simple, accurate, and effective indoor localization framework combining strengths of trilateration and fingerprinting. Given a signal level received from an access point (AP) at target, INTRI first forms a contour given by reference points (RPs) with the same signal level, taking into account signal noise. The target is hence at the juncture of contours formed by all APs. We present selecting RPs for random signal by a width parameter determining the signal contour width (or spread). Then, an LP-based formulation finds the location following spirit of trilateration, which minimizes distance between target position and all contours. A novel particle filter leverages crowdsourced user inputs to adaptively estimate the width parameter. An online algorithm is further used to calibrate heterogeneous smartphones. Our extensive experiments in an airport, a shopping mall, and our campus show INTRI outperforms recent schemes with substantially lower error (often by more than 20 percent).

SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization

To improve the accuracy of fingerprint-based localization, one may fuse step counter with fingerprints. However, the walking step model may vary among people. Such user heterogeneity may lead to measurement error in walking distance. Previous works often require a step counter tediously calibrated offline or through explicit user input. Besides, as device heterogeneity may introduce various signal readings, these studies often need to calibrate the fingerprint RSSI model. Many of them have not addressed how to jointly calibrate the above heterogeneities and locate the user. We propose SLAC, a novel system which simultaneously localizes the user and calibrates the sensors. SLAC works transparently, and is calibration-free with heterogeneous devices and users. Its novel formulation is embedded with sensor calibration, where location estimations, fingerprint signals, and walking motion are jointly optimized with resultant consistent and correct model parameters. To reduce the localization search scope, SLAC first maps the target to a coarse region (say, floor) via stacked denoising autoencoders and then executes the fine-grained localization. Extensive experimental trials at our campus and the international airport further confirm that SLAC accommodates device and user heterogeneity, and outperforms other state-of-the-art fingerprint-based and fusion algorithms by lower localization errors (often by more than 30 percent).