Information theoretic approach to robust multi-Bernoulli sensor control

TL;DR

This paper introduces an information theoretic sensor control method based on a Multi-Bernoulli framework, designed for multi-target tracking without prior clutter or detection knowledge, using Rènyi divergence as the reward function.

Contribution

It presents a novel sensor control approach that handles unknown clutter and detection profiles within a multi-target tracking context, employing Rènyi divergence for decision making.

Findings

Successfully tracks five maneuvering targets in uncertain environments

Demonstrates robustness without prior clutter or detection knowledge

Uses Monte Carlo sampling for reward computation

Abstract

A novel sensor control solution is presented, formulated within a Multi-Bernoulli-based multi-target tracking framework. The proposed method is especially designed for the general multi-target tracking case, where no prior knowledge of the clutter distribution or the probability of detection profile are available. In an information theoretic approach, our method makes use of R\`{e}nyi divergence as the reward function to be maximized for finding the optimal sensor control command at each step. We devise a Monte Carlo sampling method for computation of the reward. Simulation results demonstrate successful performance of the proposed method in a challenging scenario involving five targets maneuvering in a relatively uncertain space with unknown distance-dependent clutter rate and probability of detection.