Reweighted smoothed l<inf>0</inf>-norm based DOA estimation for MIMO radar
|dc.contributor.author||Juwono, Filbert Hilman|
|dc.identifier.citation||Liu, J. and Zhou, W. and Juwono, F.H. and Huang, D. 2017. Reweighted smoothed l<inf>0</inf>-norm based DOA estimation for MIMO radar. Signal Processing. 137: pp. 44-51.|
© 2017 Elsevier B.V. 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.
|dc.title||Reweighted smoothed l<inf>0</inf>-norm based DOA estimation for MIMO radar|
|curtin.accessStatus||Fulltext not available|
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