Target Tracking by Particle Filtering in Binary Sensor Networks

• Published in: IEEE transactions on signal processing 2008-06, Vol.56 (6), p.2229-2238
• Main Authors: Djuric, P.M., Vemula, M., Bugallo, M.F.
• Format: Article
• Language: English
• Subjects: Applied sciences; Binary sensor networks; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filtering; Information, signal and communications theory; particle filtering; Particle filters; Particle tracking; posterior CramÉr-Rao bounds (PCRBs); Sensor fusion; Sensor phenomena and characterization; Signal and communications theory; Signal processing; Signal processing algorithms; Signal, noise; Target tracking; Telecommunications and information theory; Trajectory; Wireless sensor networks
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2007.916140

We present particle filtering algorithms for tracking a single target using data from binary sensors. The sensors transmit signals that identify them to a central unit if the target is in their neighborhood; otherwise they do not transmit anything. The central unit uses a model for the target movement in the sensor field and estimates the target's trajectory, velocity, and power using the received data. We propose and implement the tracking by employing auxiliary particle filtering and cost-reference particle filtering. Unlike auxiliary particle filtering, cost-reference particle filtering does not rely on any probabilistic assumptions about the dynamic system. In the paper, we also extend the method to include estimation of constant parameters, and we derive the posterior Cramer-Rao bounds (PCRBs) for the states. We show the performances of the proposed methods by extensive computer simulations and compare them to the derived bounds.