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Near-optimal range and depth estimation using a vertical array in a correlated multipath environment

机译:相关多径环境中使用垂直阵列的近乎最佳距离和深度估计

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This paper proposes a near-optimal procedure to localize a single stationary source in a two-path underwater acoustic environment. The investigation is for an M-element vertical array with omnidirectional sensors. The range and depth estimators are developed using a linear least-squares technique when a set of auto- and cross-correlators is used for time difference of arrival (TDOA) estimates. A weighting matrix is derived to achieve the approximate maximum likelihood (ML) performance of the weighted least-squares range and depth estimators. The expressions for error variances and covariances of the range and depth estimates are derived with a small error analysis technique. It is verified analytically that the error covariance matrix of the weighted least-squares solutions reaches the Cramer-Rao lower bound in the small error region. The correlation of the range and depth estimation errors is investigated. Results show that the range and depth estimation errors are highly correlated in a multipath environment. The accuracy properties of the proposed multipath localization procedure are analyzed using different array configurations. The results show that the performances of the range and depth estimators are significantly improved if the linear-dependent TDOA estimates are included for localizing and that the unweighted range and depth estimators, using the entire set of TDOAs, are approximately optimal for most of the applications. The theoretical development of error variance and covariance expressions of the range and depth estimates, which incorporates the correlation in the TDOA estimates, is corroborated with Monte Carlo simulations.
机译:本文提出了一种在两路径水下声学环境中定位单个固定声源的近最佳程序。该研究是针对具有全向传感器的M元素垂直阵列。当将一组自相关和互相关器用于到达时间差(TDOA)估计时,将使用线性最小二乘技术开发距离和深度估计器。得出加权矩阵以实现加权最小二乘范围和深度估计器的近似最大似然(ML)性能。范围和深度估计的误差方差和协方差的表达式是使用小误差分析技术得出的。分析证明,加权最小二乘解的误差协方差矩阵在小误差区域内达到了Cramer-Rao下界。研究了距离和深度估计误差的相关性。结果表明,距离和深度估计误差在多径环境中高度相关。使用不同的阵列配置分析了所提出的多径定位过程的精度属性。结果表明,如果将线性相关的TDOA估计值用于本地化,则距离和深度估计器的性能将得到显着改善,并且使用整个TDOA集合的未加权范围和深度估计器对于大多数应用程序来说大约是最佳的。蒙特卡罗模拟证实了范围和深度估计的误差方差和协方差表达式的理论发展,其中将相关性纳入了TDOA估计中。

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