scholarly journals A Novel Two-Level Nested STAP Strategy for Clutter Suppression in Airborne Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Wei Wang ◽  
Lin Zou ◽  
Xuegang Wang
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Array Signal Processing ◽  
Space Time ◽  
Virtual Space ◽  
Temporal Smoothing ◽  
Direct Covariance ◽  
The Difference ◽  
Repetition Interval ◽  
Space Time Adaptive Processing

Nested arrays have been studied recently in array signal processing field because of their closed-form expressions for the sensor locations and achievable degrees of freedom (DOFs). In this paper, the concept of nesting is further extended to space-time adaptive processing (STAP). Different from the traditional uniform-STAP method that calculates the clutter plus noise covariance matrix (CNCM) and performs the STAP filter direct using the data snapshots collected from the uniform linear array (ULA) and the transmitting pulses with uniform pulse repetition interval (PRI), we present a new optimum two-level nested STAP (O2LN-STAP) strategy which employs an optimum two-level nested array (O2LNA) and an optimum two-level nested PRI (O2LN-PRI) to exploit the enhanced DOFs embedded in the space-time O2LN structure. Similar to the difference coarray perspective, we first construct a virtual space-time snapshot from the direct covariance matrix of the received signals. Then, a new CNCM estimation corresponding to the virtual space-time snapshot can be computed by the spatial-temporal smoothing technique for STAP filter. Furthermore, the comparative simulations and analyses with the traditional uniform-STAP and the recently reported coprime-STAP are carried out to verify the effectiveness of the O2LN-STAP approach.

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 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.

Method to Estimate and Promote SNR of Covariance Matrix in Array Signal Processing

2013 ◽  
Vol 397-400 ◽  
pp. 2156-2160
Author(s):  
Yi Ran Shi ◽  
Yan Tao Tian ◽  
Hong Wei Shi ◽  
Lan Xiang Zhu
Keyword(s):  
Signal Processing ◽  
Covariance Matrix ◽  
Array Signal Processing ◽  
Doa Estimation ◽  
Accurate Estimation ◽  
Music Algorithm ◽  
Important Work ◽  
Variance Matrix ◽  
Eigen Value ◽  
The Difference

Estimation for direction of arrival (DOA) is an important work in array signal processing, and subspace method such as MUSIC algorithm is basic and important in DOA estimation. This paper analyzes the structure of eigen value of variance matrix, and proposes a method to estimate the signal noise ratio (SNR) of the data received by sensor array. With the accurate estimation for SNR, we can estimate the work environment and decide detect threshold for many algorithm. The paper also proposes a method to promote the SNR of covariance matrix with moving the covariance slice to do DOA estimation. It can efficiently widen the difference of signal eigen value and noise eigen value.

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