Improved maximum likelihood location estimation accuracy in wireless sensor networks using the Cross-Entropy method

This paper considers the problem of target location estimation in a wireless sensor network based on IEEE 802.15.4 radio signals and proposes a novel implementation of the maximum likelihood (ML) location estimator based on the Cross-Entropy (CE) method. In the proposed CE method, the ML criterion is translated into a stochastic approximation problem which can be solved effectively. Simulation results comparing the performance of a ML target estimation scheme employing the conventional Newton method and the conjugate gradient method are presented. The simulation results show that the proposed CE method provides higher location estimation accuracy throughout the sensor field.