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.

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 An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar

2021 ◽  
Vol 13 (23) ◽  
pp. 4954
Author(s):  
Luo Zuo ◽  
Jun Wang ◽  
Jinxin Sui ◽  
Nan Li
Keyword(s):  
Frequency Domain ◽  
Target Detection ◽  
Detection Performance ◽  
Bistatic Radar ◽  
Time Varying ◽  
Clutter Suppression ◽  
Range Resolution ◽  
Novel Method ◽  
Subband Processing ◽  
Passive Bistatic Radar

Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods.

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.

Posterior Cramér–Rao lower bounds for passive bistatic radar tracking with uncertain target measurements

2013 ◽  
Vol 93 (12) ◽  
pp. 3528-3540 ◽  
Author(s):  
Pietro Stinco ◽  
Maria S. Greco ◽  
Fulvio Gini ◽  
Alfonso Farina
Keyword(s):  
Lower Bounds ◽  
Bistatic Radar ◽  
Radar Tracking ◽  
Passive Bistatic Radar
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