来自固定声纳传感器的方位和航向跟踪器

Rong Yang, G. W. Ng, Ning Ma, C. S. Chia
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引用次数: 4

摘要

本文介绍了一种利用静止声纳传感器的单方位测量实现目标方位和航向跟踪的算法。由于单个静止的纯方位传感器不可能获得目标位置,因此该传感器的最佳输出是高质量的方位轨迹。传统的基于扩展卡尔曼滤波(EKF)的纯方位跟踪器(BOT)性能不佳。这是因为EKF对初始状态很敏感,而在纯方位检测中初始状态很难估计。在BHT算法中,由一系列测量的方位计算目标航向,该航向与测量的方位组成测量向量,状态向量与扩展卡尔曼滤波相同,用于跟踪目标虚拟位置和速度,并根据跟踪的位置和速度计算最终的方位和航向值。仿真结果表明,BHT算法在航迹精度方面具有良好的性能,并且伪航迹较少。除了提供高质量的方位轨迹外,BHT还提供航向信息,这些信息可以进一步用于多传感器环境下的目标定位,这是一个非常有用的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bearing and Heading Tracker from a Stationary Sonar Sensor
In this paper, we introduce a target Bearing and Heading Tracking algorithm (BHT) using bearing-only measurement from a stationary sonar sensor. Since the target location is impossible to be obtained from a single stationary bearing-only sensor, the optimal output of this sensor is the quality bearing tracks. The conventional Bearing-Only Tracker (BOT) using an Extended Kalman Filter (EKF) does not perform well. This is because EKF is sensitive to the initial state, and the initial state is difficult to estimate in bearing-only detection. In BHT algorithm, a target heading is computed from a sequence of measured bearings, and this heading form the measurement vector together with measured bearing The state vector is the same as the extended Kalman Filter to track target dummy location and velocity, and the final bearing and heading values are computed from tracked location and velocity. The simulation results show that BHT algorithm is with good performance in terms of track accuracy, and with less false tracks. Besides the quality bearing tracks, the BHT also provides heading information which can be further utilized, as a very useful feature, on target localization in multiple sensors environment.
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