Target detection in bistatic radar networks: Node placement and dynamic frequency selection

2012 ◽  
Author(s):  
Ling Tang ◽  
Xiaowen Gong ◽  
Jianhui Wu ◽  
Junshan Zhang
Keyword(s):  
Target Detection ◽  
Bistatic Radar ◽  
Node Placement ◽  
Frequency Selection ◽  
Radar Networks ◽  
Dynamic Frequency

Target Detection in Bistatic Radar Networks: Node Placement and Repeated Security Game

2013 ◽  
Vol 12 (3) ◽  
pp. 1279-1289 ◽  
Author(s):  
Ling Tang ◽  
Xiaowen Gong ◽  
Jianhui Wu ◽  
Junshan Zhang
Keyword(s):  
Target Detection ◽  
Bistatic Radar ◽  
Node Placement ◽  
Radar Networks ◽  
Security Game

scholarly journals Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

2021 ◽  
Vol 13 (4) ◽  
pp. 701 ◽  
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Bin Tian
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
Time Integration ◽  
Detection Performance ◽  
Bistatic Radar ◽  
Detection Range ◽  
Robust Detection ◽  
Coherent Integration ◽  
Long Time ◽  
Long Time Integration

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

Target Detection Using Radar Networks

2020 ◽  
pp. 111-171
Author(s):  
Hai Deng ◽  
Zhe Geng
Keyword(s):  
Target Detection ◽  
Radar Networks

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.

Target Detection for Passive Bistatic Radar With Direct-Path Interference

2018 ◽  
Author(s):  
Yongsheng Zhao ◽  
Dexiu Hu ◽  
Chuang Zhao ◽  
Yongjun Zhao ◽  
Zhixin Liu
Keyword(s):  
Target Detection ◽  
Bistatic Radar ◽  
Direct Path ◽  
Passive Bistatic Radar
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