Reduced-dimension space-time adaptive processing based on angle-Doppler correlation coefficient

Li, Ruiyang; Li, Jun; Zhang, Wei; He, Zishu

doi:10.1186/s13634-016-0395-2

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…Thus, the training samples which might be contaminated by target signal have to be discarded [22]. Plenty of methods to discard samples contaminated by target signal have been proposed [23], this paper adopts the method of correlated coefficient to discard contaminated samples [24]. The correlated coefficient of the training sample , kl…”

Section: Discarding Samples Contaminated By Target Signalmentioning

confidence: 99%

Robust clutter suppression in heterogeneous environments based on multi frames and similarities

Duan

Deng

et al. 2021

Multidim Syst Sign Process

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A method of robust clutter suppression with space time adaptive processing (STAP) for airborne radar in heterogeneous environments is proposed, which is based on multi frames and the similarity between the cell under test and each training sample. The proposed method deals with the problem of covariance matrix estimation for radar signal processing, and it provides a solution to overcome the performance degradation of STAP in heterogeneous environments and training samples limitation. Firstly, the method expands the set of training samples by selecting similar training frames from past frames. Secondly, initial training samples are selected from the expended training samples set, which is composed by the samples of the current frame and past frames. Thirdly, initial training samples which may be contaminated by target signal are discarded. Fourthly, the similarities between the cell under test and the remaining training samples are estimated, and training samples which are more similar with the cell under test take higher weight in the estimation of clutter covariance matrix. The accuracy of the estimated clutter character is improved significantly, and thus the performance of clutter suppression is improved. Experimental results based on measured data demonstrate the performance of the proposed method.

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Section: Discarding Samples Contaminated By Target Signalmentioning

confidence: 99%

Robust clutter suppression in heterogeneous environments based on multi frames and similarities

Duan

Deng

et al. 2021

Multidim Syst Sign Process

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“…Moreover, eigenvalue decomposition in RR-STAP strategies is computationally expensive. In order to reduce the number of snapshots and the computational complexity simultaneously, reduced-dimension (RD) STAP strategies have been presented [7]- [9]. Nonetheless, the nonadaptive construction of projection matrix, which depends on the selection of intuitive experience, degrades the detection performance [1].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Learning STAP via Spatial Smoothness and Group Sparsity Regularizations

et al. 2022

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In practical applications, limited independent and identically distributed training snapshots brings a serious challenge in space-time adaptive processing (STAP), especially in the nonhomogeneous environments. Motivated by the significant spatial smoothness and sparsity commonality of weight vectors among related STAP tasks, we propose a novel STAP algorithm based on multi-task learning. In the proposed algorithm, the weight vectors corresponding to neighboring range bins of interest are kept consistent, and all weight vectors are constrained to share a common feature. Then, an alternating direction method of multipliers (ADMM) is used to solve the proposed algorithm, and the convergence of the algorithm is guaranteed. In addition, in case that the feature matrix is unknown or we want to learn a better feature matrix so that the associations among STAP tasks can be enhanced, we also provide an extension of the proposed algorithm to jointly optimize the feature matrix and weight matrix. Simulation results demonstrate the effectiveness of the proposed strategies.

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“…Unlike JDL processing, which assumes that the angular-Doppler channels surrounding the main channel are most important, the conventional auxiliary channel STAP selects auxiliary channels along the clutter ridge. There are some papers about the application of this clutter suppression method in airborne radar [24][25][26][27][28][29][30][31][32]; however, there is no literature on the application of this method in shipborne HFSWR.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Based Auxiliary Channel STAP for Target Detection in Shipborne HFSWR

et al. 2021

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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.

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Reduced-dimension space-time adaptive processing based on angle-Doppler correlation coefficient

Cited by 10 publications

References 25 publications

Robust clutter suppression in heterogeneous environments based on multi frames and similarities

Robust clutter suppression in heterogeneous environments based on multi frames and similarities

Multi-Task Learning STAP via Spatial Smoothness and Group Sparsity Regularizations

A Knowledge-Based Auxiliary Channel STAP for Target Detection in Shipborne HFSWR

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