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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Recursive Least-Squares Source Tracking using One Acoustic Vector Sensor
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Recursive Least-Squares Source Tracking using One Acoustic Vector Sensor

机译：使用一个声学矢量传感器的递归最小二乘源跟踪

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摘要

An acoustic vector-sensor (a.k.a. vector-hydrophone) is composed of three acoustic velocity-sensors, plus a collocated pressure-sensor, all collocated in space. The velocity-sensors are identical, but orthogonally oriented, each measuring a different Cartesian component of the three-dimensional particle-velocity field. This acoustic vector-sensor offers an azimuth-elevation response that is invariant with respect to the source's center frequency or bandwidth. This acoustic vector-sensor is adopted here for recursive least-squares (RLS) adaptation, to track a single mobile source, in the absence of any multipath fading and any directional interference. A formula is derived to preset the RLS forgetting factor, based on the prior knowledge of only the incident signal power, the incident source's spatial random walk variance, and the additive noise power. The work presented here further advances a multiple-forgetting-factor (MFF) version of the RLS adaptive tracking algorithm, that requires no prior knowledge of these aforementioned source statistics or noise statistics. Monte Carlo simulations demonstrate the tracking performance and computational load of the proposed algorithms.
机译：声矢量传感器（又称矢量水听器）由三个声速传感器以及一个并置的压力传感器组成，所有传感器都并置在空间中。速度传感器是相同的，但方向正交，每个传感器测量三维粒子速度场的不同笛卡尔分量。该声矢量传感器提供相对于源的中心频率或带宽不变的方位角升高响应。在没有任何多径衰落和任何方向性干扰的情况下，此声学矢量传感器在这里采用递归最小二乘（RLS）自适应，以跟踪单个移动源。基于仅对入射信号功率，入射源的空间随机游动方差和加性噪声功率的先验知识，可以得出一个公式来预设RLS遗忘因子。此处介绍的工作进一步推进了RLS自适应跟踪算法的多遗忘因子（MFF）版本，该版本不需要这些源统计信息或噪声统计信息的先验知识。蒙特卡洛仿真证明了所提出算法的跟踪性能和计算量。

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