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首页> 外文会议>2nd International Conference on Advanced Computer Control >Probability hypothesis densities for multi-sensor, multi-target tracking with application to acoustic sensors array
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Probability hypothesis densities for multi-sensor, multi-target tracking with application to acoustic sensors array

机译：多传感器，多目标跟踪的概率假设密度及其在声传感器阵列中的应用

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摘要

Random sets theory offers a uniform framework for the multi-source data fusion, and all problems of the data fusion could be describe, analyzed and solved in this framework. The multi-sensor multi-target tracking problem could be natural represented in the framework. It is of engineering importance to tracking low altitude moving targets with acoustic methods due to the blindness of the traditional radar detecting. In this paper, an algorithm for tracking the low altitude or ground moving targets is put forward based on the Probability Hypothesis Density (PHD) Filter. The PHD Filter based on Finite Set Statistics doesn't need consider data association for multi-target tracking, which propagates the PHD or first moment instead of the full multi-target posterior, and it could estimating the unknown and time-varying number of targets and their states under clutter environment. In the practical, we use the Sequential Monte Carlo (SMC) method to approximate the PHD. The paper presents a novel and fundamentally well-grounded framework for tracking multiple acoustic targets using PHD Filter and passive acoustic localization technique. Simulations are also presented to demonstrate the performance in tracking a randomly varying number of targets in a clutter environment.
机译：随机集理论为多源数据融合提供了一个统一的框架，并且可以在该框架中描述，分析和解决所有数据融合问题。多传感器多目标跟踪问题可以自然地在框架中表示出来。由于传统雷达检测的盲目性，利用声学方法跟踪低空移动目标具有工程上的重要性。本文提出了一种基于概率假设密度（PHD）滤波器的低空或地面运动目标跟踪算法。基于有限集统计信息的PHD过滤器无需考虑数据关联即可进行多目标跟踪，它可以传播PHD或第一时刻，而不是传播完整的多目标后验，因此可以估算未知且随时间变化的目标数目及其混乱状态下的状态。在实际中，我们使用顺序蒙特卡罗（SMC）方法来近似PHD。本文提出了一种新颖的，基础良好的框架，可使用PHD滤波器和无源声学定位技术来跟踪多个声学目标。还提供了仿真来演示在杂乱环境中跟踪随机变化数量的目标的性能。

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