多传感器-多目标仅测方位传感器注册
Multisensor--Multitarget Bearing--Only Sensor Registration
AI总结：
本文研究了仅测方位传感器中偏移偏差建模及其多目标跟踪中的补偿问题，提出了一种基于多传感器的算法，并推导了Cramér-Rao下界以评估方法的理论精度。
摘要：
仅测方位估计是目标跟踪中的基本且具有挑战性的问题。与雷达跟踪类似，存在偏移或位置偏差会加剧仅测方位估计的难度。建模各种传感器偏差并非易事，尤其在仅测方位跟踪方面文献较少。本文针对仅测方位传感器中的偏移偏差建模及其多目标跟踪中的偏差补偿问题，提出了一种基于多传感器的算法，能够有效处理监控区域内时间变化的目标数量。所提出的算法导致最大似然偏差估计器，并推导了Cramér-Rao下界以量化所提方法或任何其他算法可达到的理论精度。最后，通过不同分布式跟踪场景的仿真结果展示所提方法的能力。为展示所提方法即使在存在假警和漏检的情况下仍能工作，还展示了在集中式跟踪场景中本地传感器发送所有测量值（而非AMR或本地跟踪）的仿真结果。
英文摘要：
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 corresponding Cramér--Rao Lower Bound to quantify the theoretical accuracy that can be achieved by the proposed method or any other algorithm is also derived. Finally, simulation results on different distributed tracking scenarios are presented to demonstrate the capabilities of the proposed approach. In order to show that the proposed method can work even with false alarms and missed detections, simulation results on a centralized tracking scenario where the local sensors send all their measurements (not AMRs or local tracks) are also presented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
