Multi-SOI based particle filter for distributed estimation in ad hoc wireless sensor network
Fatma Aounallah, R. Amara Boujemâa, M. Turki-Hadj Alouane · 2014
This paper deals with particle filters (PF) based on quantized innovation for distributed estimation (DE) in ad hoc wireless sensor network (WSN) within a robot tracking context. When only one bit is exchanged per instant between sensors the sign of innovation particle filter (SOI-PF) has a good tracking ability. However, we propose in this work to show that processing of more than one SOI information coming from a set of neighboring sensors, simultaneously, is more performant than using a multi level quantized innovation on the same number of bits. The tracking performance of the so resulted multi-SOI PF, presented here in the context of nonlinear robot's motion tracking, are compared then to the multi level quantized innovation PF (MLQI-PF), which is derived in this work for a nonlinear and non gaussian tracking context. Simulations show that the MSOI-PF algorithm outperforms the MLQI-PF, moreover, the MSOI-PF's performance with only two binary information are similar to that of the PF based on a non quantized innovation.