Advanced integration of WIFI and inertial navigation systems for indoor mobile positioning
EURASIP Journal on Applied Signal Processing
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
OLS: opportunistic localization system for smart phones devices
Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
Restarting particle filters: an approach to improve the performance of dynamic indoor localization
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Adaptive motion model for a smart phone based opportunistic localization system
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Optimizing trajectories of mobile beacons to localize sensor networks
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Experimental analysis of IEEE 802.15.4a CSS ranging and its implications
Computer Communications
Adaptive radio maps for pattern-matching localization via inter-beacon co-calibration
Pervasive and Mobile Computing
Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals
Journal of Signal Processing Systems
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Particle Filter (PF) techniques has been widely used in indoor localization systems. They are often used in conjunction with pattern matching based on Received Signal Strength Indication (RSSI) fingerprinting. Several variants of the particle filter within a generic framework of the Sequential Importance Sampling (SIS) algorithm have been described. The purpose of this paper is to show how a variant of PF, the so-called Backtracking Particle Filter (BPF), can be used to improve indoor localization performance. The BPF is a technique for refining state estimates based on exclusion of invalid particle trajectories. Categorization of invalid trajectory determined during importance sampling step of the PF. The BPF can also take advantage of available building plan information using the so-called Map Filtering (MF) technique. The incorporation of MF allows the BPF to exploit long-range geometrical constraints. This paper evaluates BPF with indoor localization based on WLAN RSSI fingerprinting. The filtering schema is evaluated using the propagation simulation in an office building, a typical environment for fingerprinting technique. Favorable result are obtained, showing positioning performance (1.34 m mean 2D error) superior to the PF-only no MF case (1.82 m mean 2D error), or up to 25% improvement. It is also shown that the performance is far better than the position estimates from conventional Nearest-Neighbour (NN) and Kalman Filter (KF) approaches using the same RSSI measurements.