This paper investigates subspace-based space-time adaptive processing (STAP) in airborne frequency diversity array (FDA) radar systems for homogeneous clutter under range ambiguity. The key point of subspace-based FDA-STAP is to accurately estimate the clutter rank and the clutter subspace. To tackle this issue, a subspace transformation matrix is constructed in the transmit-time-receive domain according to the element spacing ratio of transmit and receive arrays, the number of Doppler ambiguity, and the number of range ambiguity, which can model the radar scene accurately. Based on the proposed subspace transformation matrix, the clutter rank is accurately derived for all element spacing ratios, the number of Doppler ambiguity, and the number of range ambiguity, and then the clutter subspace is reliably estimated. With the estimated clutter subspace, a subspace-based FDA-STAP method is provided. Furthermore, the maximum resolvable number of range ambiguity is derived. Numerical results verify that the derived clutter rank and the maximum resolvable number of range ambiguity are accurate, and the proposed FDA-STAP method has great performance advantages over related methods.

This correspondence discusses a space-time adaptive processing (STAP) method using deep neural network (DNN) and shrinkage algorithm (SA) under small training samples. A DNN-based SA is developed to estimate the clutter-plus-noise covariance matrix (CNCM) from the sample covariance matrix. This technique establishes a learning-based nonlinear mapping between the sample eigenvalues and the optimal shrunk eigenvalues and thus provides an accurate CNCM estimation. Specifically, a DNN consisting of cascaded convolutional neural network (CNN) and fully connected network (FCN) is designed, where the CNN performs the feature extraction and the FCN derives the estimation of the shrunk eigenvalues. To improve the CNN performance, a deep residual learning strategy is introduced into CNN. Due to the lack of labeled data, an airborne radar system is simulated for the construction of dataset consisting of sample eigenvalues and optimal shrunk eigenvalues. Numerical results demonstrate that the proposed DNN-based SA STAP has superior performance in comparison with the state-of-the-art SA STAP methods.

Space-time adaptive processing (STAP) is one of the most effective approaches for clutter suppression in airborne radar systems. However, when the number of training samples is less than two times system degrees-of-freedom (DoFs), the performance of conventional full-dimension STAP degrades severely due to inaccurate clutter-plus-noise covariance matrix (CNCM) estimation. In order to enhance the STAP performance under the situation of insufficient training samples, this paper proposes a novel STAP method based on the random matrix theory (RMT). According to the spiked covariance model in RMT, we can obtain the estimate of inverse CNCM by determining its eigenvalues including the noise-related ones and the clutter-related ones from the eigenvalues of sample covariance matrix (SCM) while keeping its eigenvectors consistent with that of SCM. Specifically, the noise-related eigenvalues are directly determined according to the noise power, while the clutter-related eigenvalues are determined by minimizing the output clutter-plus-noise power in terms of RMT. Ultimately, the optimal RMT-STAP weight is derived according to the classical minimum variance distortionless response criterion. Profiting from the great estimation performance on the inverse CNCM, RMT-STAP is significantly superior to other-related full-dimension STAP methods when the number of training samples is less than two times system DoFs. Numerical and real-data experiments verify the effectiveness and robustness of the proposed RMT-STAP method.

Sea clutter suppression is a hot topic for airborne radar. Space‐time adaptive processing (STAP) is a useful approach to address this issue. Currently, range ambiguity is a problem to restrict conventional STAP performance. The conventional STAP cannot differentiate the signals originating from distinct ambiguous areas due to the lack of range‐associated degrees‐of‐freedom (DoFs). Frequency diverse array (FDA) can provide the DoFs via introducing a frequency shifting between adjacent transmit elements, then FDA‐STAP is developed. According to the Reed, Mallet and Brennan (RMB) rule, FDA‐STAP requires more training samples in comparison with conventional STAP. However, the number of training samples is limited practically, and FDA‐STAP will suffer from severe performance deterioration. To address this issue, this paper introduces a sparsity recovery algorithm, atomic norm minimisation (ANM), into FDA‐STAP for clutter profile recovery, that is, ANM‐FDA‐STAP, thereby reducing the requirement on training samples. Numerical results verify that the ANM‐FDA‐STAP algorithm exhibit outstanding performance.

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm.

When airborne radar detects targets, space-time adaptive processing (STAP) performs a vital role in clutter rejection. As is known, clutter rejection is limited by range ambiguity. With range-related degrees-of-freedom, frequency diverse array (FDA) has been validated to have the ability to address this issue, and FDA-STAP flourishes. As the DoFs increase, more training samples are necessitated to satisfy the RMB rule for accurate clutter covariance matrix (CCM) estimation. Nonetheless, this rule is hard to satisfy practically, then the STAP performs poorly. To this end, this work introduces shrinkage estimation into the STAP, developing FDA-SA-STAP. First, the eigen-decomposition is implemented to obtain sample eigenvalues and eigenvectors. Then the noise eigenvalues are manipulated as noise power, and the clutter ones are adjusted via the radar output power minimization. Ultimately, a new inverse CCM is reliably derived, and the STAP can be effectively conducted. Numerical experiments confirm the performance advantages of the proposed method in different circumstances.