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Event-averaged maximum likelihood estimation and mean-field theoryin multitarget tracking

机译：多目标跟踪中的事件平均最大似然估计和均值场理论

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摘要

This paper presents a novel type of Kalman filter for track maintenance in multitarget tracking using thresholded sensor data at high target/clutter densities and low detection levels. The filter is robust against tracking errors induced by crossing tracks, clutter, and missed detections, and the computational complexity of the filter scales well with problem size. There are two key features that differentiate this approach from earlier work. First, to reduce computational load, the filter exploits techniques from statistical field theory to simplify measurement to track association by using a mean-field approximation to sum over associations. Second, to enhance tracking of close together targets, the filter explicitly models the error correlations that occur between such target pairs. These error correlations are caused by measurement to track association ambiguities that arise when target separations are comparable to sensor measurement errors
机译：本文提出了一种新型的卡尔曼滤波器，用于在高目标/杂波密度和低检测水平下使用阈值传感器数据进行多目标跟踪中的跟踪维护。该滤波器具有强大的鲁棒性，可以防止因交叉轨道，混乱和错过的检测而导致的跟踪误差，并且滤波器的计算复杂度可以随着问题的大小而适当扩展。有两个关键特征使此方法与早期工作区分开。首先，为了减少计算量，该过滤器利用了统计场理论的技术，通过使用均值场近似对关联求和来简化测量以跟踪关联。其次，为了增强对紧密目标的跟踪，过滤器显式地对在这些目标对之间发生的误差相关性进行建模。这些误差相关性是由测量引起的，以跟踪目标间隔与传感器测量误差相当时出现的关联歧义

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