A deterministic least-squares approach to space-time adaptive processing (STAP)

Abstract

A direct data domain (D 3) least-squares space-time adaptive processing (STAP) approach is presented for adaptively enhancing signals in a nonhomogeneous environment. The nonhomogeneous environment may consist of nonstationary clutter and could include blinking jammers. The D 3 approach is applied to data collected by an antenna array utilizing space and in time (Doppler) diversity. Conventional STAP generally utilizes statistical methodologies based on estimating a covariance matrix of the interference using data from secondary range cells. As the results are derived from ensemble averages, one filter (optimum in a probabilistic sense) is obtained for the operational environment, assumed to be wide sense stationary. However, for highly transient and inhomogeneous environments the conventional statistical methodology is difficult to apply. Hence, the D 3 method is presented as it analyzes the data in space and time over each range cell separately. The D 3 method is deterministic in approach. From an operational standpoint, an optimum method could be a combination of these two diverse methodologies. This paper represents several new D 3 approaches. One is based on the computation of a generalized eigenvalue for the signal strength and the others are based on the solution of a set of block Hankel matrix equations. Since the matrix of the system of equations to be solved has a block Hankel structure, the conjugate gradient method and the fast Fourier transform (FFT) can be utilized for efficient solution of the adaptive problem. Illustrative examples presented in this paper use measured data from the multichannel airborne radar measurements (MCARM) database to detect a Sabreliner in the presence of urban, land, and sea clutter. An added advantage for the D 3 method in solving real-life problems is that simultaneously many realizations can be obtained for the same solution for the signal of interest (SOI). The degree of variability amongst the different results can provide a confidence level of the processed results. The D 3 method may also be used for mobile communications.

Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection

“…Under this condition, even though the sample demand of inde-pendent distribution characteristic is satisfied by the proposed training sample selection scheme, the demand for the identical distribution characteristic will be more difficult to be met simultaneously in the nonhomogeneous clutter condition. The nonhomogeneous clutter suppression methods are not detailed herein [24][25][26][27][28], since we mainly focus on the SCNR-enhanced sub-band STAP in this part. We further derive the weight vector of the sub-band Σ∆-STAP as…”

Parametric GLRT for multichannel adaptive signal detection in space–time correlated compound‐Gaussian disturbance

“…The non‐whitening form of (72) is denoted as LS (ignoring that the disturbance is coloured). The estimator in [19] which can operate when is denoted as D3. Figs.…”

“…In [18], another two‐step PGLRT detector without any a priori knowledge about the disturbance statistics is presented, but cannot be implemented in training‐free case, which is also our focused aspect. In [19], a direct data domain (D3) least‐squares STAP approach utilises the dimension‐reduced sub‐blocks to suppress the disturbance, and can also operate in a non‐homogeneous environment without training data.…”

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

“…Several low-sample methods have been developed to relieve the performance degradation caused by limited training data, such as reduced-dimension (RD) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ] algorithms, reduced-rank (RR) [ 17 , 18 , 19 , 20 , 21 ] algorithms, parametric adaptive matched filter (PAMF) algorithms [ 22 , 23 ], direct data domain (D3) [ 24 , 25 ] algorithms and knowledge-aided (KA) algorithms [ 26 , 27 , 28 , 29 , 30 ]. Although these algorithms can reduce the number of required training samples, they suffer from some drawbacks.…”