A RADAR SIGNAL CFAR DETECTION METHOD WITH SOFT THRESHOLD

2005 ◽  
pp. 414-419
Author(s):  
YOU HE ◽  
TAO SU FENG JIAN ◽  
BINGRONG LI
Keyword(s):  
Detection Method ◽  
Radar Signal ◽  
Cfar Detection ◽  
Soft Threshold

scholarly journals Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5569 ◽  
Author(s):  
Lesya Anishchenko ◽  
Andrey Zhuravlev ◽  
Margarita Chizh
Keyword(s):  
Wavelet Transform ◽  
Input Data ◽  
Detection Method ◽  
Fall Detection ◽  
Modern Medicine ◽  
Radar Signal ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Representation ◽  
Life Threatening

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

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).

scholarly journals A novel design of algorithm and framework for decentralized CFAR signal detection without prior information

2018 ◽  
Vol 189 ◽  
pp. 04006
Author(s):  
Nan Wang ◽  
Yunshan Xu ◽  
Haibao Xia ◽  
Jundi Wang
Keyword(s):  
Signal Detection ◽  
Prior Information ◽  
Detection Method ◽  
Detection Algorithm ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Fusion Algorithm ◽  
Cfar Detection ◽  
Novel Design ◽  
Fusion Detection

In this paper, a fusion detection algorithm that focuses on decentralized CFAR (Constant False Alarm Rate) signal detection problem without prior information is proposed. In the algorithm, the threshold and test statistic of the detection fusion algorithm derive from the conventional CFAR detection method. At last a framework for decentralized CFAR signal detection is designed corresponding to the fusion algorithm. Simulation results illustrate that an almost optimal detection performance is obtained by the proposed algorithm.

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

scholarly journals LEO-Based Satellite Constellation for Moving Target Detection

2022 ◽  
Vol 14 (2) ◽  
pp. 403
Author(s):  
Chongdi Duan ◽  
Yu Li ◽  
Weiwei Wang ◽  
Jianguo Li
Keyword(s):  
Detection Method ◽  
Rapid Development ◽  
Contrast Ratio ◽  
Detection Threshold ◽  
Weighted Likelihood ◽  
Cfar Detection ◽  
Satellite Constellation ◽  
Satellite Platform ◽  
Leo Satellites ◽  
Fusion Detection

With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm.

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.

Optimization and application of LPI radar signal detection method

2014 ◽  
Vol 22 (11) ◽  
pp. 3122-3128
Author(s):  
李娜 LI Na ◽  
王珂 WANG Ke ◽  
李保珠 LI Bao-zhu
Keyword(s):  
Signal Detection ◽  
Detection Method ◽  
Radar Signal

scholarly journals Incoherent Integration Detection Method of Airborne Phased Array Radar in a Multipath Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yili Hu ◽  
Yongbo Zhao ◽  
Sheng Chen
Keyword(s):  
Phased Array ◽  
Detection Method ◽  
Detection Threshold ◽  
Constant False Alarm Rate ◽  
Detection Model ◽  
Cfar Detection ◽  
Phased Array Radar ◽  
Multipath Signal ◽  
Multipath Environment ◽  
Airborne Phased Array Radar

Airborne phased array radar (PAR) suffers from multipath problems when flying over a calm sea surface. The existence of a multipath phenomenon will cause the electromagnetic echo of the same target to be reflected back to the airborne PAR from two paths, namely, direct path (DP) and multipath. Compared with the ground-based radar, the target echo received by airborne PAR in the multipath environment has two important characteristics: one is that the DP signal and the multipath signal exist in different range bins, and the other is that the radar cross section (RCS) in the DP direction may be smaller than that in the multipath direction. Considering these two characteristics, this paper first proposes a target pairing algorithm for matching the DP range and multipath range of the same target in signal detection, and then, combined with the cell-averaging constant false alarm rate (CA-CFAR) detection model, an incoherent integration detection method for airborne PAR in the multipath environment is proposed. In the target pairing process, the geometric structure relationship of the airborne PAR model can be fully utilized. After a successful target pairing process, the energy of the multipath signal will be incoherently accumulated into the corresponding DP range bin, so as to improve the probability of DP range bin data passing the detection threshold. In essence, the proposed method makes full use of multipath energy to improve the detection capability of airborne PAR in the multipath environment. Finally, the detection probability of the proposed method is given, and the detection performance is analyzed.

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