Improved maximum likelihood estimation of target position in wireless sensor networks using particle swarm optimization

Estimation of target position from multi-frame binary data provided by a wireless sensor network (WSN) can be done by optimizing a complex multimodal likelihood function. Deterministic quasi Newton-Raphson (QNR) schemes with line search are typically used for optimization in maximum likelihood estimation. However, these methods often find a local minimum, which leads to large estimation errors. This paper presents an approach that employs particle swarm optimization (PSO) techniques for global optimization of the likelihood function. Simulation results comparing the performance of a maximum likelihood target position estimation scheme employing QNR and PSO algorithms are presented. It is seen that the PSO algorithm provides significantly higher position estimation accuracy throughout the sensor field.