Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Reducing the Calibration Effort for Probabilistic Indoor Location Estimation
IEEE Transactions on Mobile Computing
A new class of particle filters for random dynamic systems with unknown statistics
EURASIP Journal on Applied Signal Processing
Monte Carlo localization for mobile wireless sensor networks
Ad Hoc Networks
Target tracking based on a distributed particle filter in underwater sensor networks
Wireless Communications & Mobile Computing - Underwater Sensor Networks: Architectures and Protocols
ZebraNet and beyond: applications and systems support for mobile, dynamic networks
CASES '08 Proceedings of the 2008 international conference on Compilers, architectures and synthesis for embedded systems
Underground coal mine monitoring with wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Performance of some metaheuristic algorithms for localization in wireless sensor networks
International Journal of Network Management
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
Target Tracking by Particle Filtering in Binary Sensor Networks
IEEE Transactions on Signal Processing
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
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Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non-Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re-sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. The quality prediction function is proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, a centroid shift vector is calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.