scholarly journals Weak Target Detection Method of Passive Bistatic Radar Based on Probability Histogram

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Panhe Hu ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Scattered Wave ◽  
Detection Methods ◽  
Radar Signal ◽  
Bistatic Radar ◽  
Electronic Countermeasure ◽  
Passive Bistatic Radar ◽  
Weak Target

Passive bistatic radar (PBR) has attracted widespread attention for its capabilities in dealing with the threat of electronic countermeasure, stealth technology, and antiradiation missile. However, passive detection methods are limited by unknown characteristics of the uncooperative illuminators, and conventional radar signal processing algorithms cannot be conducted accurately, especially when the carrier frequency of the transmitting signal is agile and the signal-to-noise ratio (SNR) in the scattered wave of target is low. To address the above problems, this paper presents a novel weak target detection method based on probability histogram, which is then tested by a field experiment. Preliminary results indicate the feasibility of the proposed method in weak target detection.

scholarly journals Target detection method in passive bistatic radar

2020 ◽  
Vol 31 (3) ◽  
pp. 510-519
Author(s):  
Chen Gang ◽  
Wang Jun
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Bistatic Radar ◽  
Passive Bistatic Radar

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 Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

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

scholarly journals Experimental results of maritime moving target detection based on passive bistatic radar using non-cooperative radar illuminators

2019 ◽  
Vol 2019 (20) ◽  
pp. 6763-6766 ◽  
Author(s):  
Song Jie ◽  
Cai Fu-qing ◽  
Zhang Cai-sheng ◽  
He You
Keyword(s):  
Target Detection ◽  
Experimental Results ◽  
Bistatic Radar ◽  
Moving Target ◽  
Moving Target Detection ◽  
Passive Bistatic Radar

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

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