基于重加权l_1范数惩罚的远近场混合源定位算法

Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l_1-Norm Penalty

现有信源定位方法大多假定信源是远场源或近场源,而实际定位系统中往往存在远场源和近场源共存的情况.为实现远、近场源分离及高精度信源定位,本文在稀疏信号重构理论框架下提出了一种新的远近场混合源定位算法.该算法利用阵列协方差矩阵反对角线元素和重加权l_1范数惩罚获得所有信源的到达角(Direction Of Arrival,DOA)估计.在DOA估计的基础上,根据远场与近场源距离参数位于不同区间的特点利用一维搜索实现远、近场源分离以及近场源距离参数的估计.从理论角度分析了重加权l_1范数惩罚算法的重构性能.本文所提算法不仅同时适用于高斯和非高斯信号,而且无需多维搜索和参数配对,也无需信源数的先验信息,同时还可以获得较好的定位精度.计算机仿真结果验证了所提算法的有效性.

Existing source localization methods mostly assume that the sources are pure near-field sources or pure farfield sources. While in practical localization systems,both far-field and near-field sources may exist simultaneously. To classify far-field and near-field sources,and also to achieve high-precision source localization,a novel mixed far-field and nearfield source localization algorithm is proposed in sparse signal reconstruction framework. The algorithm first utilizes anti-diagonal elements of array covariance matrix and reweighted l1-norm penalty to obtain DOA estimation of all sources,then classifies far-field and near-field sources and successively obtains range estimation of near-field sources via one-dimensional search,by exploring the feature that the range parameters of far-field and near-field sources are located in different areas.Theoretically,we analyze the reconstruction performance of the reweighted l1-norm penalty algorithm. The proposed algorithm is not only suitable for dealing with Gaussian signals and non-Gaussian signals,but also without multi-dimensional search and parameter pairing process,and also without knowing the number of sources. Meanwhile,the proposed algorithm can even provide good estimation accuracy. Computer simulation results validate the effectiveness of the proposed algorithm.