# Multi-Sensor Control for Multi-Object Bayes Filters

## TL;DR

This paper introduces a fast, multi-sensor control algorithm for multi-object tracking using a POMDP framework, leveraging labeled multi-Bernoulli filters and coordinate descent for efficient multi-dimensional optimization.

## Contribution

It presents a novel multi-sensor control method based on POMDPs and labeled multi-Bernoulli filters, with a coordinate descent approach for efficient optimization and multi-sensor fusion.

## Key findings

- Significantly faster than existing methods
- Maintains similar tracking accuracy
- Effective in scenarios with multiple moving targets

## Abstract

Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a novel approach to the multi-target multi-sensor control problem within the partially observed Markov decision process (POMDP) framework. We model the multi-object state as a labeled multi-Bernoulli random finite set (RFS), and use the labeled multi-Bernoulli filter in conjunction with minimizing a task-driven control objective function: posterior expected error of cardinality and state (PEECS). A major contribution is a guided search for multi-dimensional optimization in the multi-sensor control command space, using coordinate descent method. In conjunction with the Generalized Covariance Intersection method for multi-sensor fusion, a fast multi-sensor algorithm is achieved. Numerical studies are presented in several scenarios where numerous controllable (mobile) sensors track multiple moving targets with different levels of observability. The results show that our method works significantly faster than the approach taken by a state of art method, with similar tracking errors.