基于稀疏信号重构的阵元位置误差校正方法
发布时间：2018-12-11 18:46
【摘要】：阵元位置误差的存在会严重影响水听器阵列的测向性能,为此在使用阵列之前需对该误差进行校正。针对这一需求,提出了一种对任意阵型适用的高精度阵元位置有源校正方法。结合远场阵列模型以及位置误差"有界"的特点,基于压缩感知理论,将阵元位置估计转化为对稀疏信号的重构过程,建立了阵元位置误差模型,构造了该模型下凸优化函数,并采用l_1-SVD(Singular Value Decomposition,SVD)方法进行求解,同时给出了物理解释和算法实施步骤。计算机仿真表明基于稀疏信号重构的校正方法性能明显高于子空间拟合算法,且性能接近相应克拉美罗下界,对于校正源方位误差有较强的容错能力,不受制于微小误差的假设以及初值的选取。该方法是一种可对阵列中的部分或全部阵元进行校正的高精度、稳健的有源校正算法。
[Abstract]:The location error of array element will seriously affect the performance of hydrophone array, so the error must be corrected before using the array. In order to meet this demand, a high precision active position correction method for arbitrary matrix is proposed. Combining the characteristics of far-field array model and position error "bounded", based on the theory of compression perception, the position estimation of array element is transformed into the reconstruction process of sparse signal, and the position error model of array element is established, and the convex optimization function under the model is constructed. The l_1-SVD (Singular Value Decomposition,SVD method is used to solve the problem, and the physical explanation and the steps of algorithm implementation are given. Computer simulation shows that the performance of the correction method based on sparse signal reconstruction is obviously higher than that of the subspace fitting algorithm, and the performance is close to the corresponding Clamero lower bound, so it has a strong fault tolerance ability for correcting the azimuth error of the source. It is not subject to the assumption of minor errors and the selection of initial values. This method is a high precision and robust active correction algorithm which can correct some or all elements of the array.
【作者单位】： 哈尔滨工程大学水声技术重点实验室;哈尔滨工程大学水声工程学院;
【基金】：国家自然科学基金项目(51279043,11504064) 水声技术国家重点实验室基金项目(9140C2000501150C200)资助
【分类号】：TN911.7
[Abstract]:The location error of array element will seriously affect the performance of hydrophone array, so the error must be corrected before using the array. In order to meet this demand, a high precision active position correction method for arbitrary matrix is proposed. Combining the characteristics of far-field array model and position error "bounded", based on the theory of compression perception, the position estimation of array element is transformed into the reconstruction process of sparse signal, and the position error model of array element is established, and the convex optimization function under the model is constructed. The l_1-SVD (Singular Value Decomposition,SVD method is used to solve the problem, and the physical explanation and the steps of algorithm implementation are given. Computer simulation shows that the performance of the correction method based on sparse signal reconstruction is obviously higher than that of the subspace fitting algorithm, and the performance is close to the corresponding Clamero lower bound, so it has a strong fault tolerance ability for correcting the azimuth error of the source. It is not subject to the assumption of minor errors and the selection of initial values. This method is a high precision and robust active correction algorithm which can correct some or all elements of the array.
【作者单位】： 哈尔滨工程大学水声技术重点实验室;哈尔滨工程大学水声工程学院;
【基金】：国家自然科学基金项目(51279043,11504064) 水声技术国家重点实验室基金项目(9140C2000501150C200)资助
【分类号】：TN911.7
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