GM-PHD Filter for Searching and Tracking an Unknown Number of Targets with a Mobile Sensor with Limited FOV

TL;DR

This paper introduces a Bayesian RFS-based GM-PHD filter framework for searching and tracking an unknown number of moving targets with a limited FOV sensor, capable of estimating target count, density, and trajectories.

Contribution

It generalizes the GM-PHD filter for simultaneous search and tracking with limited FOV sensors, including non-linear trajectory prediction using Gaussian Processes.

Findings

Effective in estimating target number and density.

Able to track non-linear target trajectories.

Validated through simulations and real aerial robot data.

Abstract

We study the problem of searching for and tracking a collection of moving targets using a robot with a limited Field-Of-View (FOV) sensor. The actual number of targets present in the environment is not known a priori. We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for tracking problems to allow for simultaneous search and tracking with a limited FOV sensor. The proposed framework can extract individual target tracks as well as estimate the number and the spatial density of targets. We also show how to use the Gaussian Process (GP) regression to extract and predict non-linear target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations and a real data collected from an aerial robot.