A particle-filter information potential method for tracking and monitoring maneuvering targets using a mobile sensor agent

Abstract

The problem of tracking and monitoring moving targets using mobile sensor agents (MSAs) is relevant to a variety of applications, including monitoring of endangered species, civilian security, and military surveillance. This paper presents a new information potential field approach for computing the motion plans and control inputs of a MSA, based on the feedback obtained from a modified particle filter used for tracking multiple moving targets in a region of interest. A modified particle filter is presented that implements a new sampling method based on supporting intervals of normal probability density functions. The method accounts for the latest sensor measurements by adapting a mixture representation of the target probability density functions (PDFs). The target motion is modeled as a semi-Markov jump process, such that the target PDFs, or the PDFs of the Markov parameters, can be updated based on real-time sensor measurements by a centralized processing unit or MSAs supervisor. A new information potential method is presented that computes an artificial potential function based on the output of the modified particle filter. Using this artificial potential, the sensors compute feedback control inputs that allow them to track and monitor a maneuvering target over time, using a bounded field of view (FOV).