Mutual information, Fisher information, and population coding
Neural Computation
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Sensor management using an active sensing approach
Signal Processing
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
IEEE Transactions on Signal Processing
Adaptive Polarized Waveform Design for Target Tracking Based on Sequential Bayesian Inference
IEEE Transactions on Signal Processing
Target Location Estimation in Sensor Networks With Quantized Data
IEEE Transactions on Signal Processing
A particle algorithm for sequential Bayesian parameter estimationand model selection
IEEE Transactions on Signal Processing
Target Tracking by Particle Filtering in Binary Sensor Networks
IEEE Transactions on Signal Processing
A Jump Markov Particle Filter for Localization of Moving Terminals in Multipath Indoor Scenarios
IEEE Transactions on Signal Processing - Part I
Large-Scale Optimal Sensor Array Management for Multitarget Tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
In this paper, the source localization problem in wireless sensor networks is investigated where the location of the source is estimated based on the quantized measurements received from sensors in the field. An energy efficient iterative source localization scheme is proposed where the algorithm begins with a coarse location estimate obtained from measurement data from a set of anchor sensors. Based on the available data at each iteration, the posterior probability density function (pdf) of the source location is approximated using an importance sampling based Monte Carlo method and this information is utilized to activate a number of nonanchor sensors. Two sensor selection metrics namely the mutual information and the posterior Cramér-Rao lower bound (PCRLB) are employed and their performance compared. Further, the approximate posterior pdf of the source location is used to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that with significantly less computation, the PCRLB based iterative sensor selection method achieves similar mean squared error (MSE) performance as compared to the state-of-the-art mutual information based sensor selection method. By selecting only the most informative sensors and compressing their data prior to transmission to the fusion center, the iterative source localization method reduces the communication requirements significantly and thereby results in energy savings.