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PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter

机译：杂波中多目标多传感器声纳跟踪的PMHT方法

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摘要

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.
机译：多传感器声纳跟踪具有许多优势，例如可以减少总体测量不确定性以及隐藏接收机的可能性。然而，由于水下环境的复杂性，特别是低目标检测概率和混响引起的虚假警报数量巨大，使用多目标多传感器声纳跟踪具有挑战性。在这项工作中，为了解决在杂波情况下的多目标多传感器声纳跟踪问题，基于扩展卡尔曼滤波器（EKF）和无味卡尔曼滤波器（UKF）的新型概率多假设跟踪器（PMHT）方法被提议。 PMHT可以有效地处理未知的测量目标和测量数据与发射器之间的数据关联歧义。 EKF和UKF用于处理测量模型中的高度非线性。仿真结果表明，该算法在杂乱环境下可以大大提高目标跟踪性能，计算量较小。

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