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Two-Dimensional Angle Estimation for Monostatic MIMO Radar Using Expanded PARAFAC Model

Dong-Mei Huang, Jian-Zhong Xu, and Ding Li
Information Department, Naval Command College, Nanjing, 210016, China

Abstract—In this research, we address the problem of Two-Dimensional (2D) angle estimation for monostatic Multiple-Input Multiple-Output (MIMO) radar. An expanded PARAFAC model is proposed to make full use of the Vandermonde-like structure of the data model, which expands the received data through unitary transformation and links the problem of 2D angle estimation to the PARAFAC model. Unlike the traditional estimation algorithms such as multiple signal classification (MUSIC) and estimation method of signal parameters via rotational invariance techniques (ESPRIT), the proposed algorithm does not require spectral peak searching nor eigenvalue decomposition of the received signal covariance matrix. Furthermore, our algorithm can achieve automatic pairing of 2D angles, and it has blind and robust characteristic, therefore the proposed algorithm has higher working efficiency. In addition, the proposed algorithm can detect more targets and has better estimation accuracy than ESPRIT algorithm and PARAFAC method. Extensive numerical experiments verify the effectiveness and improvement of our algorithm.

Index Terms—multiple-input multiple-output radar, angle estimation, PAFAFAC decomposition

Cite: Dong-Mei Huang, Jian-Zhong Xu, and Ding Li, "Two-Dimensional Angle Estimation for Monostatic MIMO Radar Using Expanded PARAFAC Model," Journal of Communications, vol. 12, no. 1, pp. 32-39, 2017. Doi: 10.12720/jcm.12.1.32-39