scholarly journals A Novel Clutter Suppression Method Based on Sparse Bayesian Learning for Airborne Passive Bistatic Radar with Contaminated Reference Signal

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6736
Author(s):  
Jipeng Wang ◽  
Jun Wang ◽  
Yun Zhu ◽  
Dawei Zhao
Keyword(s):  
Covariance Matrix ◽  
Bayesian Learning ◽  
Reference Signal ◽  
Space Time ◽  
Bistatic Radar ◽  
Sparse Bayesian Learning ◽  
Clutter Suppression ◽  
Sensing Technology ◽  
Blind Area ◽  
Passive Bistatic Radar

The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated.

Range-Doppler Spectrograms-Based Clutter Suppression of HF Passive Bistatic Radar by D-CycleGAN

2021 ◽  
pp. 1-1
Author(s):  
Zhixin Zhao ◽  
Xin Chen ◽  
Bo Li ◽  
Yuhao Wang ◽  
Qiegen Liu
Keyword(s):  
Bistatic Radar ◽  
Clutter Suppression ◽  
Passive Bistatic Radar

scholarly journals Sparse Bayesian Learning-Based Space-Time Adaptive Processing With Off-Grid Self-Calibration for Airborne Radar

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 47296-47307 ◽  
Author(s):  
Huadong Yuan ◽  
Hong Xu ◽  
Keqing Duan ◽  
Wenchong Xie ◽  
Weijian Liu ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Space Time ◽  
Airborne Radar ◽  
Sparse Bayesian Learning ◽  
Adaptive Processing ◽  
Self Calibration ◽  
Space Time Adaptive Processing

scholarly journals Removal of Cochannel Interference Using Probabilistic Latent Component Analysis in Passive Bistatic Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tao Ying ◽  
Xuebao Wang ◽  
Wei Tian ◽  
Cheng Zhou
Keyword(s):  
Target Detection ◽  
Reference Signal ◽  
Component Analysis ◽  
Cochannel Interference ◽  
Training Data ◽  
Bistatic Radar ◽  
Frequency Channel ◽  
Novel Approach ◽  
Passive Bistatic Radar

This paper examines the problem of cancellation of cochannel interference (CCI) present in the same frequency channel as the signal of interest, which may bring a reduction in the performance of target detection, in passive bistatic radar. We propose a novel approach based on probabilistic latent component analysis for CCI removal. The highlight is that removing CCI is considered as reconstruction, and extraction of Doppler-shifted and time-delayed replicas of the reference signal exploited fully as training data. The results of the simulation show that the developed method is effective.

scholarly journals Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR

2021 ◽  
Vol 13 (17) ◽  
pp. 3429
Author(s):  
Yingjie Miao ◽  
Jingchun Li ◽  
Yao Bao ◽  
Feifeng Liu ◽  
Cheng Hu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Public Security ◽  
Bistatic Radar ◽  
Surveillance Systems ◽  
Digital Audio ◽  
Clutter Suppression ◽  
Theoretical Derivation ◽  
Data Segmentation ◽  
Passive Bistatic Radar ◽  
Advanced Version

The increasing accessibility of unmanned aerial vehicles (UAVs) drives the demand for reliable, easy-to-deploy surveillance systems to consolidate public security. This paper employs passive bistatic radar (PBR) based on a digital audio broadcast (DAB) satellite for UAV monitoring in applications with power density limitations on electromagnetic radiation. An advanced version of the extensive cancellation algorithm (ECA) based on data segmentation and coefficients filtering is designed to improve the efficiency of multipath clutter suppression while retaining robustness, for which the effectiveness is verified by theoretical derivation and simulation. The detectability of small UAVs with DAB satellite-based PBR is validated with experimental results, with which the influence of target altitude and bistatic geometry are also analyzed.

scholarly journals A Joint Low-Rank and Sparse Method for Reference Signal Purification in DTMB-Based Passive Bistatic Radar

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3607
Author(s):  
Luo Zuo ◽  
Jun Wang ◽  
Te Zhao ◽  
Zuhan Cheng
Keyword(s):  
Channel Estimation ◽  
Reference Signal ◽  
Temporal Correlation ◽  
Low Rank ◽  
Estimation Accuracy ◽  
Bistatic Radar ◽  
Purification Method ◽  
Noise Component ◽  
Pn Sequences ◽  
Passive Bistatic Radar

In a digital terrestrial multimedia broadcasting (DTMB)-based passive bistatic radar (PBR) system, the received reference signal often suffers from serious multipath effect, which decreases the detection ability of low-observable targets in urban environments. In order to improve the target detection performance, a novel reference signal purification method based on the low-rank and sparse feature is proposed in this paper. Specifically, this method firstly performs synchronization operations to the received reference signal and thus obtains the corresponding pseudo-noise (PN) sequences. Then, by innovatively exploiting the inherent low-rank structure of DTMB signals, the noise component in PN sequences is reduced. After that, a temporal correlation (TC)-based adaptive orthogonal matching pursuit (OMP) method, i.e., TC-AOMP, is performed to acquire the reliable channel estimation, whereby the previous noise-reduced PN sequences and a new halting criterion are utilized to improve channel estimation accuracy. Finally, the purification reference signal is obtained via equalization operation. The advantage of the proposed method is that it can obtain superior channel estimation performance and is more efficient compared to existing methods. Numerical and experimental results collected from the DTMB-based PBR system are presented to demonstrate the effectiveness of the proposed method.

Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar

2017 ◽  
Vol 130 ◽  
pp. 159-168 ◽  
Author(s):  
Zetao Wang ◽  
Wenchong Xie ◽  
Keqing Duan ◽  
Yongliang Wang
Keyword(s):  
Bayesian Learning ◽  
Airborne Radar ◽  
Sparse Bayesian Learning ◽  
Clutter Suppression

Multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V191-V199 ◽  
Author(s):  
Ming Ma ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Jingjing Wang ◽  
Junxiang Wen
Keyword(s):  
Covariance Matrix ◽  
Seismic Data ◽  
Bayesian Learning ◽  
Expectation Maximization Algorithm ◽  
Spatial Relationship ◽  
Real Data ◽  
Sparse Bayesian Learning ◽  
Seismic Profiles ◽  
Spatially Correlated ◽  
Reflectivity Inversion

The reflectivity inversion approach based on a variety of regularization terms was extensively developed and applied to image subsurface structure in recent years. In addition, multichannel reflectivity inversion or deconvolution considering the lateral continuity of reflection interfaces or reflectivity in adjacent channels has been developed. However, these processing operations seldom adaptively judge the stratal continuity or automatically alter the parameters of the corresponding algorithm. To use the special correlation of the reflection information contained in the seismic data, a multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning (bSBL) is introduced. The method adopts a covariance matrix that describes the spatial relationship of reflectivity and simultaneously controls the temporal sparsity. With an expectation-maximization algorithm, we can obtain the parameters of the multichannel reflectivity model, including the mean (i.e., the estimated multichannel reflectivity) and the covariance matrix (i.e., the correlation of nonzero reflection impulses). The noise variance in the observed seismic data is also estimated during the inversion processing. Due to the contribution of reflectivity correlation in different traces, the performance of the multichannel spatially correlated reflectivity inversion using bSBL is significantly superior to the trace-by-trace processing method in the presence of a medium level of noise. The synthetic and real data examples illustrate that the lateral continuity is well-preserved in seismic profiles after inversion.

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