An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

Abstract

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios.

A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar

“…where η > 0 is the regularisation parameter balancing the datafitting term kX − ΦAk 2 F versus the sparse penalty term kAk 2;0 . Nevertheless, solving the above optimisation problem is NP-hard [36], so many recently proposed algorithms have been developed to efficiently find sparse solutions [24][25][26][27].…”

“…Proof: To see the equivalence of the above two problems, we consider the Schur complement of the constraint of Equation (27).…”

“…Motived by the burgeoning development of compressed sensing techniques, sparse recovery theory was first applied to estimate a high-resolution clutter spectrum by Maria in 2006 [16], demonstrating great potential for tackling the sample shortage problem. Since then, numerous sparse recovery-based STAP (SR-STAP) algorithms have been developed [17][18][19][20][21][22][23][24][25][26][27][28][29], exploiting the intrinsic sparsity of the clutter spectrum. Studies have shown that SR-STAP algorithms can outperform traditional statistical STAP methods with limited training samples (even with a single sample) in ideal cases [26,27].…”

“…Since then, numerous sparse recovery-based STAP (SR-STAP) algorithms have been developed [17][18][19][20][21][22][23][24][25][26][27][28][29], exploiting the intrinsic sparsity of the clutter spectrum. Studies have shown that SR-STAP algorithms can outperform traditional statistical STAP methods with limited training samples (even with a single sample) in ideal cases [26,27]. Depending on the adopted sparse signal model, SR-STAP can be further classified into two categories, namely, grid-based methods [17][18][19][20][21][22][23][24][25][26][27] and gridless methods [28,29].…”

“…Studies have shown that SR-STAP algorithms can outperform traditional statistical STAP methods with limited training samples (even with a single sample) in ideal cases [26,27]. Depending on the adopted sparse signal model, SR-STAP can be further classified into two categories, namely, grid-based methods [17][18][19][20][21][22][23][24][25][26][27] and gridless methods [28,29].…”