Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Int. J. Pervasive Comput. Commun. Pub Date : 2021-11-23 DOI:10.1108/ijpcc-06-2021-0138

M. Lakshmi, S. Rao, K. Subrahmanyam

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引用次数: 3

Abstract

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

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移位瑞利滤波:一种新的基于方位和仰角测量的三维普适水下被动目标跟踪估计滤波算法

目的海洋探测正成为普适计算水下目标跟踪的重要组成部分。目前文献中发现了许多普遍存在的技术，但对其在目标跟踪中的有效性的研究却很少。本文介绍了一种用于水下三维目标跟踪的移位瑞利滤波器(SHRF)。比较了SHRF和先前证明的无气味卡尔曼滤波(UKF)方法。研究发现，与UKF相比，shrf特别适用于远程场景，用于跟踪解决方案收敛性较低的目标。在分析中，考虑了从单个运动目标的方位、仰角测量噪声中确定目标位置和速度的问题。针对目标运动分析方法，生成了SHRF并对其性能进行了评价。原创性/价值建议的过滤器比UKF执行得更好，特别是对于长期场景。用MATLAB给出了蒙特卡罗的实验结果，结果表明SHRF技术的增强效果是明显的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Int. J. Pervasive Comput. Commun.

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