Training samples selection method for space‐time adaptive processing based on clutter covariance matrix reconstruction

2017 ◽  
Vol 53 (14) ◽  
pp. 953-954 ◽  
Author(s):  
Zhihui Li ◽  
Yongshun Zhang ◽  
Hanwei Liu ◽  
Yiduo Guo
Keyword(s):  
Covariance Matrix ◽  
Selection Method ◽  
Space Time ◽  
Adaptive Processing ◽  
Matrix Reconstruction ◽  
Training Samples ◽  
Samples Selection ◽  
Covariance Matrix Reconstruction ◽  
Space Time Adaptive Processing

Space-Time Adaptive Processing and Jamming

2018 ◽  
pp. 38-62
Keyword(s):  
Signal Processing ◽  
Computational Complexity ◽  
Covariance Matrix ◽  
Basic Concept ◽  
Processing Technique ◽  
Space Time ◽  
Adaptive Processing ◽  
Signal Processing Technique ◽  
Space Time Adaptive Processing ◽  
Established Technique

Space-time adaptive processing (STAP) has been a well-established technique, whose basic concept and theory are first put forward by Brennan and Reed. However, it is difficult to implement in the practical system because of the computational complexity and the sample limitation for estimating the clutter covariance matrix. STAP is a modern signal processing technique that can improve target detectability in the presence of a strong clutter Klemm.

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

2021 ◽  
Vol 13 (4) ◽  
pp. 621
Author(s):  
Liang Guo ◽  
Weibo Deng ◽  
Di Yao ◽  
Qiang Yang ◽  
Lei Ye ◽  
...  
Keyword(s):  
Space Time ◽  
Clutter Suppression ◽  
Sea Clutter ◽  
First Order ◽  
Knowledge Based ◽  
Training Samples ◽  
Wave Radar ◽  
Samples Selection ◽  
Space Time Adaptive Processing ◽  
Sidelobe Canceller

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.

scholarly journals A Space-Time Adaptive Processing Method Based on Sparse Reconstruction of Reverberation Interference

2020 ◽  
Vol 38 (6) ◽  
pp. 1179-1187
Author(s):  
Siwei Kou ◽  
Xi'an Feng ◽  
Hui Huang ◽  
Yang Bi
Keyword(s):  
Covariance Matrix ◽  
Cone Angle ◽  
Doppler Frequency ◽  
Space Time ◽  
Processing Method ◽  
Statistical Characteristics ◽  
Distribution Model ◽  
Sparse Reconstruction ◽  
Adaptive Processing ◽  
Space Time Adaptive Processing

Aiming at the problem of how to obtain reverberation samples and estimate their covariance matrix in the space-time adaptive processing(STAP) of sonar system, a new space-time adaptive processing method is proposed based on sparse reconstruction of reverberation in this paper. Firstly, according to the space-time distribution characteristics of reverberation received by moving platform sonar, a space-time steering dictionary for sparse reconstruction of reverberation is designed along the relation curve between Doppler frequency shift and incident cone angle cosine of the reverberation unit. Then, a reverberation sample in the rangecell under test (RUT) is reconstructed with high precision by sparse decomposition of signals obtained from the sonar array in the space-time steering dictionary. Finally, based on the prior information of reverberation probability distribution model, a sufficient number of reverberation samples are generated to meet the requirement of performance loss index on reverberation sample size in the space-time adaptive processing, so as to correctly obtain estimation of the covariance matrix of reverberation. This method can reconstruct the reverberation samples and estimate the reverberation covariance matrix directly from the data in RUT without relying on the auxiliary data from units adjacent to the RUT. Therefore, it is not only suitable for the environment with constant reverberation statistical characteristics, but also suitable for the environment with varying statistical characteristics. Simulation results of sonar forward-looking array and side-looking array indicate that the improvement factor of the proposed method is about 10dB lower than the traditional space-time adaptive processing method. So this new STAP method has good anti-reverberation performance.

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