A Practical Bias Estimation Algorithm for Multisensor--Multitarget Tracking

TL;DR

This paper introduces a new bias estimation algorithm for multisensor-multitarget tracking that performs well even with intermittent data and without needing local filter gains, improving practical distributed tracking accuracy.

Contribution

The paper presents a bias estimation algorithm that functions effectively without local filter gain information and with intermittent track transmissions in distributed systems.

Findings

Algorithm approaches the performance of exact bias estimation methods.

Effective with non-consecutive track data transmissions.

Uses tracklet concept to improve practical bias estimation.

Abstract

Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most previously proposed algorithms for bias estimation rely on local measurements in centralized systems or tracks in distributed systems, along with additional information like covariances, filter gains or targets of opportunity. In addition, it is generally assumed that such data are made available to the fusion center at every sampling time. In practical distributed multisensor tracking systems, where each platform sends local tracks to the fusion center, only state estimates and, perhaps, their covariances are sent to the fusion center at non-consecutive sampling instants or scans. That is, not all the information required for exact bias estimation at the fusion center is available in practical distributed tracking systems. In this paper, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed tracking systems with intermittent track transmission. Through the calculation of the Posterior Cram\'er--Rao lower bound and various simulation results, it is shown that the performance of the new algorithm, which uses the tracklet idea and does not require track transmission at every sampling time or exchange of filter gains, can approach the performance of the exact bias estimation algorithm that requires local filter gains.