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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Track labeling and PHD filter for multitarget tracking
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Track labeling and PHD filter for multitarget tracking

机译：跟踪标签和PHD过滤器，用于多目标跟踪

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摘要

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approaches (as, e.g., MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter has been proposed and particle filtering techniques have been developed to implement the PHD filter. In the particle PHD filter, the track labeling problem is not considered, i.e., the PHD is obtained only for a frame at a time, and it is very difficult to perform the multipeak extraction, particularly in high clutter environments. A track labeling method combined with the PHD approach, as well as considering the finite resolution, is proposed here for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD in the resolution cell to get the estimated number and locations of the targets at each time step, and then perform the track labeling ("peak-to-track" association), whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard particle PHD filter. For example, in group target tracking, if we are interested in the motion of a specific target, we can track this target, which is not possible for the standard particle PHD filter, since the standard particle PHD filter does not keep track labels. Using our approach, multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging, and termination
机译：多目标跟踪需要结合过滤进行数据关联。当多个目标间隔很近时，常规方法（例如MHT /分配）可能无法提供令人满意的结果。这主要是因为难以确定目标数量。最近，已经提出了概率假设密度（PHD）滤波器，并且已经开发了粒子滤波技术来实现PHD滤波器。在粒子PHD滤波器中，没有考虑轨道标记问题，即，一次仅针对一帧获得PHD，并且执行多峰提取非常困难，尤其是在高杂波环境中。本文提出了一种与PHD方法结合并考虑有限分辨率的轨道标记方法，用于多目标跟踪，即，我们为每个目标保留一个单独的跟踪器，在分辨率单元格中使用PHD获取估计的数量和位置在每个时间步中对目标进行“定位”，然后执行轨迹标记（“峰到轨迹”关联），其结果可以为下一个时间步中的PHD峰提取提供信息。此外，通过为每个目标保留单独的跟踪器，我们的方法比标准粒子PHD过滤器提供了更多的信息。例如，在组目标跟踪中，如果我们对特定目标的运动感兴趣，则可以跟踪该目标，这对于标准粒子PHD滤镜是不可能的，因为标准粒子PHD滤镜不保留跟踪标签。使用我们的方法，可以通过自动跟踪启动，维护，生成，合并和终止来执行多目标跟踪

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