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A new TMA method for state estimation of underwater target

机译:一种新的水下目标估算TMA方法

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In this paper, a novel target motion analysis (TMA) method using unscented Kalman Filter (UKF) based on modified polar coordinates (MPC) is proposed to improve the localization accuracy for underwater target with constant velocity. The proposed approach utilizes bearings of two-arrays to estimate the localization and velocity of the target. Unlike extended Kalman filter, unscented Kalman Filter (UKF), which is biased and sensitive to the initial value in Cartesian coordinate, the proposed scheme is much more stable and asymptotically unbiased. Moreover, the performances among these schemes are compared based on the noisy bearings from towed array sonar and flank array sonar via Monte Carlo simulations. Simulation results show that the proposed MPC-UKF has better performances with higher numerical stability and little cost of computational complexity.
机译:在本文中,提出了一种基于修改的极性坐标(MPC)的使用Uncented Kalman滤波器(UKF)的新型目标运动分析(TMA)方法,以提高具有恒定速度的水下目标的定位精度。所提出的方法利用双阵列的轴承来估计目标的定位和速度。与扩展卡尔曼滤波器不同,未加注的卡尔曼滤波器(UKF),其对笛卡尔坐标的初始值偏置和敏感,所提出的方案更稳定,渐近渐近。此外,这些方案之间的性能基于来自拖曳阵列声纳和侧翼阵列声纳的噪声轴承通过蒙特卡罗模拟。仿真结果表明,所提出的MPC-UKF具有更好的数值稳定性和计算复杂性成本较高的性能。

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