基于数据协方差矩阵重构的MIMO声纳DOA估计

发布时间：2018-03-12 16:38

本文选题：MIMO声纳　切入点：方位估计　出处：《应用声学》2017年02期 　论文类型：期刊论文

【摘要】：实际多输入多输出(MIMO)声纳系统由于环境或人为因素,可能出现部分阵元失效,从而导致阵列自由度减少、方位估计精度下降。本文提出了一种数据协方差矩阵重构方法,该方法基于差分阵列性质,利用正常工作阵元的协方差矩阵元素来恢复失效阵元的矩阵元素,获得满秩的数据协方差矩阵,从而恢复到全阵元MIMO声纳的阵列自由度。与已有方法相比,降低了计算复杂度。仿真及海试实验数据处理结果表明,本文所提的数据协方差矩阵重构方法能够恢复因部分阵元失效而丢失的阵列自由度,应用于方位估计中,所能分辨的最大目标数与全阵元相同。
[Abstract]:Due to environmental or human factors, partial array element failure may occur in practical multi-input multi-output MIMO-sonar system, which results in the reduction of array freedom and the decrease of azimuth estimation accuracy. In this paper, a data covariance matrix reconstruction method is proposed. Based on the properties of the difference array, the covariance matrix element of the normal working element is used to restore the matrix element of the failure element, and the full rank data covariance matrix is obtained. Thus, the array degree of freedom of the full array element MIMO sonar is restored. Compared with the existing methods, the computational complexity is reduced. The results of simulation and sea trial data processing show that, The data covariance matrix reconstruction method proposed in this paper can recover the lost degree of freedom of the array due to the failure of some array elements. The maximum number of targets that can be resolved in the azimuth estimation is the same as that of the full array element.
【作者单位】：海军驻沈阳地区电子系统军事代表室;中国科学院声学研究所声场声信息国家重点实验室;中国人民解放军92330部队;中国科学院大学;
【基金】：国家自然科学基金项目(61571436,11434012,41561144006)
【分类号】：TB56

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