Reweighted smoothed l0-norm based DOA estimation for MIMO radar

Jing Liu, Weidong Zhou, Filbert H. Juwono, Defeng (David) Huang

    Research output: Contribution to journalArticlepeer-review

    18 Citations (Web of Science)

    Abstract

    In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated from the latest received data matrix obtained by a reduced dimensional transformation. Then a weighted matrix is introduced to transform the covariance estimation errors into a Gaussian white vector, and the proposed method further constructs the other reweighted vector to enhance sparse solution. Finally, a reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed, based on which the sparse solution is obtained by using a decreasing parameter sequence and the steepest ascent algorithm. Consequently, DOA estimation is accomplished by searching the spectrum of the solution. Compared with the conventional l1-norm minimization based methods, the proposed reweighted smoothed l0-norm algorithm significantly reduces the computation time of DOA estimation. The proposed method is about two orders of magnitude faster than the l1-SVD, reweighted l1-SVD and RV l1-SRACV algorithms. Meanwhile, it provides higher spatial angular resolution and better angle estimation performance. Simulation results are used to verify the effectiveness and advantages of the proposed method.

    Original languageEnglish
    Pages (from-to)44-51
    Number of pages8
    JournalIET Signal Processing
    Volume137
    DOIs
    Publication statusPublished - 1 Aug 2017

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