2D-DOA estimation with spatially separated “long” crossed-dipoles array based on three-way compressive sensing

• Published in: Multidimensional systems and signal processing Vol. 33; no. 4; pp. 1087 - 1104
• Main Authors: Li, Binbin, Zeng, Li, Liu, Weijian, Shi, Junpeng, Zhou, Bilei, Li, Zhihui, Du, Qinglei, Chen, Hui, Tang, Peiqin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Circuits and Systems; Dipoles; Direction of arrival; Electrical Engineering; Engineering; Matched pursuit; Mathematical analysis; Parameter estimation; Radar arrays; Sensor arrays; Signal to noise ratio; Signal,Image and Speech Processing; Tensors; Parameter estimation; Parallel factorization decomposition; “Long” crossed-dipoles; Three-way compressive sensing; ESPRIT method; Orthogonal matching tracking
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-022-00832-0

For the angle estimation demand of real-world electromagnetic vector sensor (EMVS) array radar, in this paper we propose a two-dimensional direction of arrival (2D-DOA) estimation method with spatially separated “long” crossed-dipoles array based on three-way compressive sensing. First, the low-dimension random matrix is utilized to compressively sample the received data in tensor style. Second, the compressed factor matrices are achieved through parallel factor decomposition. Third, the cosines’ estimations are derived from the support locations of sparse vectors recovered with orthogonal matching pursuit algorithm. Finally, 2D-DOA estimation is calculated according to the relationship between cosines. Compared with the traditional ESPRIT method, the proposed method can estimate parameters with higher accuracy under the condition of low signal-to-noise ratio and a few number of snapshots, which is conducive to improve the robustness of angle estimation with real-world EMVS array radar.