Deterministic Aided STAP for Target Detection in Heterogeneous Situations

Abstract

Classical space-time adaptive processing (STAP) detectors are strongly limited when facing highly heterogeneous environments. Indeed, in this case, representative target free data are no longer available. Single dataset algorithms, such as the MLED algorithm, have proved their efficiency in overcoming this problem by only working on primary data. These methods are based on the APES algorithm which removes the useful signal from the covariance matrix. However, a small part of the clutter signal is also removed from the covariance matrix in this operation. Consequently, a degradation of clutter rejection performance is observed. We propose two algorithms that use deterministic aided STAP to overcome this issue of the single dataset APES method. The results on realistic simulated data and real data show that these methods outperform traditional single dataset methods in detection and in clutter rejection.

A Robust Dual-Platform GMTI Method against Nonuniform Clutter

“…According to the low-rank characteristics of clutter, the reduced-rank STAP algorithm estimates the clutter subspace based on the feature space classification analysis method, which alleviates the performance loss caused by insufficient samples in a heterogeneous environment [13][14][15]. Based on this idea, two methods were proposed: the direct data domain least-squares (D3-LS) STAP method [16] and the maximum likelihood estimation detector (MLED) method [17][18][19]. Unfortunately, the benefits of these methods are generated by reducing the degree of freedom (DOF) in the system, which leads to performance degradation.…”

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

“…Since ground clutter is nonhomogeneous, finding sufficient IID secondary data for the detection processing poses the most serious challenge to successfully implementing STAP algorithms. An alternative approach for the detection problem in a heterogeneous environment where training data is severely limited or altogether unavailable is the deterministic STAP approach [ 6 – 11 ], which can remove all undesired contributions from every single range gate, and hence bypass the problem of the required homogeneous secondary data support. The well-know direct data domain (D 3 ) methods [ 6 , 7 ] and the maximum likelihood estimation detector (MLED) algorithms [ 8 – 11 ] can only operate with the data under test, so that the performance is not impacted by nonstationarity.…”

“…An alternative approach for the detection problem in a heterogeneous environment where training data is severely limited or altogether unavailable is the deterministic STAP approach [ 6 – 11 ], which can remove all undesired contributions from every single range gate, and hence bypass the problem of the required homogeneous secondary data support. The well-know direct data domain (D 3 ) methods [ 6 , 7 ] and the maximum likelihood estimation detector (MLED) algorithms [ 8 – 11 ] can only operate with the data under test, so that the performance is not impacted by nonstationarity. Furthermore, a hybrid detector that combines the single-data set (SDS) and two-data set (TDS) algorithms was proposed in [ 12 ].…”

Low-Complexity Spatial-Temporal Filtering Method via Compressive Sensing for Interference Mitigation in a GNSS Receiver

“…However, the pre-or postprocessing techniques are widely used in current interior GNSS receiver design because of low cost and complexity. The spatialtemporal adaptive processing (STAP) techniques have been proposed to perform 2D filtering on radar or navigation signals to mitigate narrowband/wideband (NB/WB) interferences, multipath, and other uncertainties [1][2][3][4][5][6][7]. Nevertheless, it is necessary to increase the number of antennas and time delay elements to obtain better antijam performance for antenna array processing [8].…”