Multisensor--Multitarget Bearing--Only Sensor Registration
Ehsan Taghavi, R. Tharmarasa, T. Kirubarajan, Mike McDonald

TL;DR
This paper introduces a novel multisensor approach for modeling and estimating offset biases in bearing-only sensors, improving multitarget tracking accuracy by compensating for sensor biases and deriving theoretical bounds.
Contribution
It proposes a maximum likelihood bias estimator for bearing-only sensors, handling time-varying targets and sensor biases, with derivation of the Cramér-Rao Lower Bound for accuracy assessment.
Findings
The algorithm effectively estimates sensor biases in simulated scenarios.
The method improves tracking accuracy with bias compensation.
Simulation results demonstrate robustness even with false alarms and missed detections.
Abstract
Bearing--only estimation is one of the fundamental and challenging problems in target tracking. As in the case of radar tracking, the presence of offset or position biases can exacerbate the challenges in bearing--only estimation. Modeling various sensor biases is not a trivial task and not much has been done in the literature specifically for bearing--only tracking. This paper addresses the modeling of offset biases in bearing--only sensors and the ensuing multitarget tracking with bias compensation. Bias estimation is handled at the fusion node to which individual sensors report their local tracks in the form of associated measurement reports (AMR) or angle-only tracks. The modeling is based on a multisensor approach that can effectively handle a time--varying number of targets in the surveillance region. The proposed algorithm leads to a maximum likelihood bias estimator. The…
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