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

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.

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APPLICATION OF DISTRIBUTED DETECTION METHOD FOR GROUND MOVING TARGETS IN SENSOR NETWORKS

International Journal of Information Acquisition ◽

10.1142/s0219878906001027 ◽

2006 ◽

Vol 03 (04) ◽

pp. 271-281

Author(s):

HONGBING CAO ◽

JIANMING WEI ◽

TAO XING ◽

HAITAO LIU

Keyword(s):

Seismic Data ◽

Detection Method ◽

Detection Algorithm ◽

Distributed Detection ◽

Sensor Nodes ◽

Constant False Alarm Rate ◽

Moving Targets ◽

Cfar Detection ◽

Computational Performance ◽

Surveillance Application

This paper considers the problem of detecting ground moving targets in a wireless sensor network for military surveillance application. The sensor nodes are severely constrained in both power and computational performance, and the battle field environments are commonly very complicated. A modified order statistic constant false alarm rate (OS-CFAR) detection algorithm and its distributed detection algorithm were chosen to be implemented on sensor nodes. The algorithm includes background estimate and adaptive threshold. The experimental results with real acoustic and seismic data indicate that the OS-CFAR is robust and flexible and distributed algorithm can improve the detection performance somewhat.

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Adjacent Infrared Multitarget Detection Using Robust Background Estimation

Journal of Sensors ◽

10.1155/2016/7279081 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

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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LEO-Based Satellite Constellation for Moving Target Detection

Remote Sensing ◽

10.3390/rs14020403 ◽

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.

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Research on the Detection of Vehicle Vibration and Noise Signal Based On Negative Entropy Detection Algorithm

Journal of Physics Conference Series ◽

10.1088/1742-6596/2074/1/012055 ◽

2021 ◽

Vol 2074 (1) ◽

pp. 012055

Author(s):

Naibin Zhai ◽

Haijun Zhao ◽

Xintao Cui

Keyword(s):

Signal Detection ◽

Detection Method ◽

Recognition Accuracy ◽

Detection Algorithm ◽

Noise Signal ◽

Noise Model ◽

Vehicle Noise ◽

Negative Entropy ◽

And Control ◽

Coding Bits

Abstract As an important part of vehicle noise signal detection and processing, negative entropy detection algorithm can accurately reduce the number of speech coding bits, ameliorate the recognition accuracy, and establish the noise model in the process of noise reduction. Based on this, this paper first analyses the source and control of vehicle vibration and noise, then studies the principle of negative entropy detection algorithm of vehicle vibration and noise signal, and finally gives the vehicle vibration and noise signal detection method based on negative entropy detection algorithm.

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Effective Semisupervised Community Detection Using Negative Information

Mathematical Problems in Engineering ◽

10.1155/2015/109671 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Dong Liu ◽

Dequan Duan ◽

Shikai Sui ◽

Guojie Song

Keyword(s):

Community Detection ◽

Prior Information ◽

Detection Method ◽

Detection Algorithm ◽

Negative Information ◽

Discovery Process ◽

The Past ◽

Considerable Research ◽

Specific Community ◽

Community Detection Algorithm

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.

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

International Journal of Antennas and Propagation ◽

10.1155/2021/8699512 ◽

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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