Multi-Scan Multi-Sensor Multi-Object State Estimation

TL;DR

This paper introduces an efficient algorithm for multi-sensor, multi-scan, multi-object state estimation that computes the exact multi-object posterior density using a Gibbs sampling-based assignment method, suitable for offline or recursive applications.

Contribution

It presents a novel, computationally feasible approach to multi-object estimation across multiple sensors and scans by solving large-scale assignment problems with Gibbs sampling.

Findings

Algorithm effectively estimates multi-object states from simulated data.

Gibbs sampling-based assignment converges efficiently.

Method applicable in both batch and recursive modes.

Abstract

If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across multiple scans. In this article, we propose an efficient numerical solution to the multi-scan multi-sensor multi-object estimation problem by computing the (labeled) multi-sensor multi-object posterior density. Minimizing the $L_{1}$-norm error from the exact posterior density requires solving large-scale multi-dimensional assignment problems that are NP-hard. An efficient multi-dimensional assignment algorithm is developed based on Gibbs sampling, together with convergence analysis. The resulting multi-scan multi-sensor multi-object estimation algorithm can be applied either offline in one batch or recursively. The efficacy of the algorithm is demonstrated using numerical experiments with a simulated dataset.