SUAS for 8-channel GMTI radar using alternative high performance non-SMI STAP algorithm

Abstract

GTRI is developing an 8-channel X-band experimental radar for a small unmanned aircraft system (SUAS) for adaptive multi-channel, MIMO, and waveform diversity test bed studies. New adaptive algorithms, one of which is covered in this paper, are also part of the test bed. Estimation of the statistical covariance matrix forms a central role in radar detection and adaptive beamforming algorithm. For example, the optimal (adaptive) linear combiner (beamformer) weights for a radar sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix for MIMO problems. Rather than form an estimate of the covariance matrix directly from the available data and inverting (sample matrix inversion [SMI]), an alternative direct estimate of the inverse may be obtained by forming parametric MC linear prediction estimates and then expressing the inverse in terms of these parametric MC estimates. The resulting parametric inverse estimate will be more accurate than inverting the estimate of the covariance matrix, leading to greatly improved detection performance over conventional methods of covariance estimation and inversion. This paper reveals the structure of the inverse of the covariance matrix for one parametric technique. The inverse structure involves products of triangular block MC Toeplitz matrices, which leads to a fast computational solution. Performance improvements over classical sample covariance matrix estimation are illustrated.