A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar

Abstract

In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

Random Nyström Approach to Clutter Subspace Estimation for Polarimetric Space-Time Adaptive Processing

“…where U e and U r denote the estimated and real matrix representation of the clutter subspace, respectively; ||•|| F denotes the Frobenius norm. Because the estimated matrix U e is not a column orthogonal matrix, the projection operator is used by U e (U H e U e ) −1 U H e in (25). Observed from the standard Nyström method in part 4.1, U e is determined by the selection matrix S. Therefore, f (U e ) is also determined by S. In the following, a new criterion is proposed to determine the selection matrix S.…”

“…It can be seen from Table 2 that the average computation time of the mLSM method is denoted as t 1 , and the average computation time of the pR-Nyström method is less than that of the other existing methods except for the pNyström method and pBi-PerEFA method. The EFA referred to in this paper is with three Doppler bins and the iteration number of pBiPerEFA method [25] is nine.…”

2-D DOA Estimation Method Based on Coprime Array MIMO Radar

“…MIMO radar uses multiple antennas to transmit different waveforms and receive reflected signals from multiple targets. Therefore, it can achieve large degrees of freedom (DOF) based on waveform diversity, thereby improving spatial resolution, enhancing parameter identification, and improving target detection performance [15,16,17,18,19]. References [20,21] combine MIMO and coprime array to estimate DOA, which improves the estimation performance of DOA, but coprime array still use uniform linear arrays to construct effective differential arrays with ideal characteristics, but this linearity Coprime array can only provide one-dimensional DOA estimation, and the accuracy of twodimensional DOA estimation is not very high.…”

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

“…Research on multiple-input multiple-output (MIMO) systems has been drawing more and more attention from both the communication and radar communities [1][2][3][4]. Especially, MIMO radar systems with distributed antennas [5] can provide spatial diversity, leading to improve localization and detection performance.…”