BCRLB Under the Fusion Extended Kalman Filter

TL;DR

This paper derives the Bayesian Cramér-Rao Lower Bound for a multi-target tracking system using probabilistic data association and an extended Kalman filter, providing theoretical performance limits in radar-based tracking.

Contribution

It introduces the derivation of the Bayesian Cramér-Rao Lower Bound within the PDA-based fusion framework for multi-target radar tracking, a novel theoretical analysis.

Findings

Derived the BCRLB under the PDA fusion framework

Provided theoretical limits for target state estimation accuracy

Enhanced understanding of tracking performance bounds

Abstract

In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cram\'er-Rao Lower Bound (BCRLB) under the PDA fusion framework.