Fast PARAFAC Algorithm for Target Localization in Bistatic MIMO Radar in the Co-Existence of Unknown Mutual Coupling and Spatially Colored Noise

作     者：Sui, Junjie Ye, Fang Wang, Xinhai Wen, Fangqing 

作者机构：Harbin Engn Univ Elect Informat Engn Harbin 150001 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China Yangtze Univ Natl Demonstrat Ctr Expt Elect & Elect Educ Jingzhou 434023 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：185720-185729页

核心收录：

基　　金：National Natural Science Foundation of China National Key Research and Development Program of China [2016YFF0102806] Natural Science Foundation of Heilongjiang Province, China [F2017004] 

主　　题：Multiple-input multiple-output radar target localization mutual coupling spatially colored noise parallel factor analysis 

摘      要：Target positioning using multiple-input multiple-output (MIMO) radar system has aroused extensive attention in the past decade. However, most of the existing positioning algorithms are only suitable for ideal scenarios (e.g., well-calibrated sensors, orthogonal waveforms, Gaussian white noise). In this paper, we focus on the target localization problem in a bistatic MIMO system in the co-existence of mutual coupling and spatially colored noise, i.e., to estimate the direction-of-arrival (DOA) and direction-of-departure (DOD) in such non-ideal scenario. To tackle this issue, a parallel factor (PARAFAC) analysis algorithm is proposed. Firstly, the de-noising operation is carried out to suppress the spatially colored noise. Then a PARAFAC analysis model is constructed to explore the tensor nature of measurement. After computing PARAFAC decomposition on the new tensor, the factor matrices associate with DOD and DOA are obtained. Thereafter, the de-coupling operation is followed to eliminate the mutual coupling. Finally, the idea of least squares is utilized to recovery DOD and DOA from the factor matrices. The proposed algorithm can achieve closed-form solution for DOD and DOA estimation without pairing calculation. Moreover, it provides better estimation performance than the state-of-the-art approaches. Detailed analyses are illustrated and simulation results verify the effectiveness of the proposed algorithm.