We consider the problem of tracking the direction of arrivals (DOA) of multiple moving targets in monostatic multiple-input multiple-output (MIMO) radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB) of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008)), but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008)). Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008)). The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar.

In this article, we consider a computationally efficient direction of departure and direction of arrival estimation problem for a bistatic multiple-input multipleoutput (MIMO) radar. The computational loads of the propagator method (PM) can be significantly smaller since the PM does not require any eigenvalue decomposition of the cross correlation matrix and singular value decomposition of the received data. An improved PM algorithm is proposed to obtain automatically paired transmit and receive angle estimations in the MIMO radar. The proposed algorithm has very close angle estimation performance to conventional PM, which has a much higher complexity than our algorithm. For high signal-to-noise ratio, the proposed algorithm has very close angle estimation to estimation of signal parameters via rotational invariance technique algorithm. The variance of the estimation error and Crame´r-Rao bound of angle estimation are derived. Simulation results verify the usefulness of our algorithm.

In this paper, we mainly discuss projection approximation and subspace tracking of deflation(PASTd) for direction of departure (DOD) and direction of arrival (DOA) tracking multiple targets in bistatic multiple-input multiple -output (MIMO) radar. The research on DOD and DOA tracking of moving targets in bistatic MIMO radar has been reported rarely. This proposed algorithm can obtain signal subspace by PASTd without computing eigen-value decomposition of the cross correlation matrix or singular value decomposition of the received data, which reduces much computational load, it also can get DOD and DOA paired without additional consideration in bistatic MIMO radar. Simulation results verify the usefulness of our algorithm in tracking moving targets.

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This paper links the polarization-sensitive-array parameter estimation problem to the quadrilinear model. Exploiting this link, it derives a blind joint angle, frequency and polarization estimation algorithm. The simulation results reveal that the proposed algorithm has better angle, frequency and polarization estimation performance than ESPRIT. This algorithm relies on a fundamental result of the uniqueness of low-rank four-way data decomposition. Furthermore, the proposed algorithm does not require pairing among multiple parameters. Simulation results illustrate performance of this algorithm.