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In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new method is based on LU distribution with partial pivoting. Simulation results confirm the validity of the presented model and theoretical assumptions. In addition, more accurate object detection results were demonstrated for specific computational examples than for other statistical methods. Considering the current analysis of the literature, it is noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Hence, it should be emphasized that the posted study fills a gap in current research on STAP. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the levels of performance of the (full or reduced versions of) space-time adaptive processing (STAP) algorithms applied to high-frequency surface wave radars (HFSWRs) for marine target detection to verify whether this family outperforms the usual moving target indication (MTI) algorithms. In fact, STAP algorithms have well-known drawbacks (e.g., large amount of secondary data). The propagation and backscattering of radar signals are included in the covariance matrix calculation. Since the propagation and backscattering have to be derived from physical features of the sea (e.g., sea Doppler) and the atmosphere, we also include the difference induced by the waveform (pulsed radar/chirp) in our study. Moreover, since the inverse covariance matrix is used in the target detection algorithms, matrix conditioning is also inspected for practical implementation reasons. We numerically detail the performance (signal noise clutter ratio and conditioning number of the STAP algorithms) with regards to the MTI performance for several configurations of radar and sea state. These results show that the improvements of signal noise clutter ratio are counterbalanced by a raise of the conditioning number, except in the case of eigencanceler approach. The sea-state parameters have few effects on the performance unlike to radar parameters. [ABSTRACT FROM PUBLISHER]

The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells. [ABSTRACT FROM AUTHOR]