An information potential approach for tracking and surveilling multiple moving targets using mobile sensor agents

The problem of surveilling moving targets using mobile sensor agents (MSAs) is applicable to a variety of fields, including environmental monitoring, security, and manufacturing. Several authors have shown that the performance of a mobile sensor can be greatly improved by planning its motion and control strategies based on its sensing objectives. This paper presents an information potential approach for computing the MSAs' motion plans and control inputs based on the feedback from a modified particle filter used for tracking moving targets. The modified particle filter, as presented in this paper implements a new sampling method (based on supporting intervals of density functions), which accounts for the latest sensor measurements and adapts, accordingly, a mixture representation of the probability density functions (PDFs) for the target motion. It is assumed that the target motion can be modeled as a semi-Markov jump process, and that the PDFs of the Markov parameters can be updated based on real-time sensor measurements by a centralized processing unit or MSAs supervisor. Subsequently, the MSAs supervisor computes an information potential function that is communicated to the sensors, and used to determine their individual feedback control inputs, such that sensors with bounded field-of-view (FOV) can follow and surveil the target over time.