RSS-based joint detection and tracking in mixed LOS and NLOS environments

This paper considers the problem of joint detection and tracking in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments by using received signal strength (RSS) measurements. A nonlinear target tracking model with multiple switching parameters has been proposed, in which multiple independent Markov chains are used to describe the switching of target maneuvers and the transition of LOS/NLOS measurements, respectively. Based on the proposed tracking model, a multi-sensor multiple model Bernoulli filter (MMBF) has been developed by employing the random finite set theory which can formulate the joint detection and tracking in a unified framework. To derive a closed-form expression to the MMBF, the Gaussian mixture implementations have been provided by applying the extended Kalman filter technique. A numerical example is provided involving tracking a maneuvering target by a sensor network with 30 nodes. Simulation results confirm the effectiveness of the proposed filter.