Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars\nrequire the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of\nreceived snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays,\nthe inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms\nwhich do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer\nnumber of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays\nwhen CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially\nfor small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian\nmatching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the\nunknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of\nsnapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is\nshown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
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