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This study presents two alternative techniques for the estimation of the target direction of arrival (DOA) for a moving radar equipped with digital beamforming, operating in look-down against strong clutter echoes. Both the considered techniques, namely AB space–time adaptive processing (AB-STAP) and generalised monopulse estimator, are based on a dual cancelled channel approach that simplifies their implementation. An extensive performance comparison is presented for the ground moving targets. Both the theoretical and simulated analyses of DOA estimation are performed, which include the comparison to the Cramér-Rao bound. The considered processing schemes are shown to yield comparable accuracies in target DOA estimation with respect to a maximum likelihood estimator. Moreover, they ensure lower computational cost, since no numerical maximisation of any functional is required. AB-STAP shows the additional nice property of estimation robustness when a limited set of homogeneous data is available to achieve the adaptivity. The comparison is finally performed applying the different estimators to a set of real multichannel data.

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l2 norm to approximate the weighted l1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations.

By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are applied here, and three improved dimension-reduced structures of 3D-STAP are proposed in this article. After analyzing the characteristics and distribution of nonstationary clutter for SBEWR, we find that the demands for auxiliary beams are different in elevation, azimuth and Doppler dimension. In addition, the suggestion to choose the number of auxiliary beams in each dimension is given. Simulation experiments are conducted to verify the analysis and evaluate the performance of the proposed methods. The simulation results show that the proposed 3D-STAP methods have better performance and lower computational burdens than typical 3D-STAP methods.

With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity.

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas.

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the relationship between elevation frequency and range. It uses an elevation oblique subspace projection technique to construct an elevation adaptive filter, which separates clutter from ambiguous regions. Finally, residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used.

A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.

The measurement errors induced by the space‐time adaptive processing (STAP) is gaining attention for its significant detriment to the precision of the global navigation satellite system (GNSS) receiver positioning. To mitigate measurement errors, the steering vector (SV) estimation method based on spreading is widely employed in measurement error mitigation algorithms. However, the hazard of the SV estimation fluctuation problem is ignored in these algorithms. In this paper, the specific harm of such SV estimation fluctuation problem is analysed. To alleviate such problem and to eliminate the measurement errors as much as possible, a robust STAP beamformer for GNSS receivers is proposed. First, to acquire a series of robust SVs in different integration times, a desired signal covariance (DSC) matrix is iteratively reconstructed to remove the disturbance from thermal noise and the residual jamming signals. Second, to eliminate measurement errors, a replacement matrix is formed to help guarantee the phase linearities of the tapped delay lines (TDLs). Numerical examples demonstrate that the method can achieve a set of stable SV estimations and linear phase TDLs, leading to a carrier‐to‐noise‐power ratio (C/N0$C/N_0$) of more than 50 dBHz and a code phase bias of less than 3.4 m, which outperform the methods used for comparison. Wideband cancellation of interference in a GPS receive array. The space‐time adaptive processing (STAP) beamformer may induce measurement errors in a satellite receiver, resulting in positioning errors. To mitigate these errors when the directions of the satellite signals are unknown, the blind mitigation method is proposed from two aspects. In the first aspect, a desired signal covariance matrix is constructed and updated every integration time to improve the stability of the satellite direction estimations; In the second aspect, the constraints of the STAP are modified by using a replacement matrix to improve the linearity behaviour of the frequency response of the temporal filters of the STAP.

The broadened first-order sea clutter in shipborne high frequency surface wave radar (HFSWR), which will mask the targets with low radial velocity, is a kind of classical space–time coupled clutter. Space–time adaptive processing (STAP) has been proven to be an effective clutter suppression algorithm for space-time coupled clutter. To further improve the efficiency of clutter suppression, a STAP method based on a generalized sidelobe canceller (GSC) structure, named as the auxiliary channel STAP, was introduced into shipborne HFSWR. To obtain precise clutter information for the clutter covariance matrix (CCM) estimation, an approach based on the prior knowledge to auxiliary channel selection is proposed. Auxiliary channels are selected along the clutter ridge of the first-order sea clutter, whose distribution can be determined by the system parameters and regarded as pre-knowledge. To deal with the heterogeneity of the spreading first-order sea clutter, an innovative training samples selection approach according to the Riemannian distance is presented. The range cells that had shorter Riemannian distances to the cell under test (CUT) were chosen as training samples. Experimental results with measured data verified the effectiveness of the proposed algorithm, and the comparison with the existing clutter suppression algorithms showed the superiority of the algorithm.