Abstract
Measurements in wireless sensor networks (WSNs) are often correlated both in space and in time. This paper focuses on tracking multiple targets in WSNs by taking into consideration these measurement correlations. A sequential Markov Chain Monte Carlo (SMCMC) approach is proposed in which a Metropolis within Gibbs refinement step and a likelihood gradient proposal are introduced. This SMCMC filter is applied to case studies with cellular network received signal strength data in which the shadowing component correlations in space and time are estimated. The efficiency of the SMCMC approach compared to particle filtering, as well as the gradient proposal compared to a basic prior proposal, are demonstrated through numerical simulations. The accuracy improvement with the gradient-based SMCMC is above $90\%$ when using a low number of particles. Thanks to its sequential nature, the proposed approach can be applied to various WSN applications, including traffic mobility monitoring and prediction.
Highlights
T RACKING multiple mobile targets is a challenging task which has applications in a number of fields, including that of wireless cellular communication networks and mobility prediction for intelligent transportation systems
Simulation results using synthetic data on the superiority of sequential Markov Chain Monte Carlo (SMCMC) with Gibbs refinement compared to Sequential Importance Resampling (SIR) with resample-move, and the benefits of this gradient proposal compared to the prior, are presented and analyzed in Section V, while Section VI highlights the main conclusions of this work
In order to obtain a more efficient algorithm for multiple target tracking, we propose an alternative solution based on a more advanced methodology known as Sequential Markov Chain Monte Carlo (SMCMC) [12]
Summary
T RACKING multiple mobile targets is a challenging task which has applications in a number of fields, including that of wireless cellular communication networks and mobility prediction for intelligent transportation systems. In order to efficiently solve the Bayesian tracking problem, we propose to use a Sequential Markov Chain Monte Carlo (SMCMC) algorithm. This technique, which is still largely under-exploited in the signal processing literature, allows for more robust and overall better performance than the more classical particle filtering, especially in high-dimensional systems [12]–[14]. We detail and justify our choice of the SMCMC methodology and complete these results by replacing the prior proposal density of the Gibbs refinement step with a likelihood gradient proposal This allows to better capitalize on informative measurements and guides the particles towards high-likelihood zones, increasing the efficiency of the algorithm. Simulation results using synthetic data on the superiority of SMCMC with Gibbs refinement compared to SIR with resample-move, and the benefits of this gradient proposal compared to the prior, are presented and analyzed in Section V, while Section VI highlights the main conclusions of this work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
