scholarly journals Random Nyström Approach to Clutter Subspace Estimation for Polarimetric Space-Time Adaptive Processing

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1488
Author(s):  
Kang Zhao ◽  
Zhiwen Liu ◽  
Shuli Shi ◽  
Yulin Huang ◽  
Yougen Xu
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Selection Procedure ◽  
Space Time ◽  
Adaptive Processing ◽  
Noise Covariance Matrix ◽  
Noise Covariance ◽  
Subspace Estimation ◽  
Simulation Results ◽  
Space Time Adaptive Processing

A random Nyström (R-Nyström) scheme for clutter subspace estimation is proposed in the context of polarimetric space-time adaptive processing (pSTAP). Unlike the standard Nyström scheme making use of only partial columns of the clutter plus noise covariance matrix (CNCM), R-Nyström exploits full CNCM information with a properly designed selection procedure under the newly developed random ridge cross leverage score (RRCLS) criterion. With R-Nyström, sup-ported by the complete CNCM columns, upgraded clutter subspace estimation can be achieved at the expense of an insignificant increase in computational complexity, in contrast to the standard Nyström. The R-Nyström-based pSTAP, termed pR-Nyström, is shown to be superior over the current eigendecomposition-free subspace pSTAP in the signal to clutter plus noise loss and computational complexity. The efficacy of R-Nyström/pR-Nyström is validated by the simulation results.

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.

Short-Range Clutter Suppression for Non-Side Looking Airborne Radar by Elevation Constraint Adaptive Processing

2014 ◽  
Vol 543-547 ◽  
pp. 2414-2417
Author(s):  
Fei He ◽  
Dong Chu Jiang
Keyword(s):  
Covariance Matrix ◽  
Short Range ◽  
Accurate Estimate ◽  
Airborne Radar ◽  
Adaptive Processing ◽  
Clutter Suppression ◽  
Noise Covariance Matrix ◽  
Noise Covariance ◽  
Eigen Decomposition ◽  
Space Time Adaptive Processing

Space-time adaptive processing (STAP) can obtain excellent clutter suppression performance based on the accurate estimate of clutter plus noise covariance matrix which is obtained by enough identical independent distributed samples. However, the short-range clutter of non-side looking airborne radar (non-SLAR) occurs range dependence. As a result, the clutter plus noise covariance matrix can not be estimated accurately and thus the clutter suppressing performance by STAP declines greatly. In this paper, an elevation constraint adaptive processing method is proposed. This method obtains the elevation target steering vector by the eigen-decomposition of the elevation covariance matrix on the ambiguous pure far-range clutter, it is used to suppress short-range clutter and the residual range-independent clutter can be suppressed by two-dimensional azimuth-Doppler STAP methods. Simulation results show validity of the proposed method.

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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