scholarly journals Two Types of Distributed CFAR Detection Based on Weighting Functions in Fusion Center for Weibull Clutter

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Amir Zaimbashi
Keyword(s):  
Fusion Center ◽  
Test Statistics ◽  
Constant False Alarm Rate ◽  
Robust Performance ◽  
Global Test ◽  
Weighting Functions ◽  
Cfar Detection ◽  
Local Detectors ◽  
Detection Schemes ◽  
Maximum Minimum

Two types of distributed constant false alarm rate (CFAR) detection using binary and fuzzy weighting functions in fusion center are developed. In the two types of distributed detectors, it was assumed that the clutter parameters at the local sensors are unknown and each local detector performs CFAR processing based on ML and OS CFAR processors before transmitting data to the fusion center. At the fusion center, received data is weighted either by a binary or a fuzzy weighting functions and combined according to deterministic rules, constructing global test statistics. Moreover, for the Weibull clutter, the expression of the weighting functions, based on ML and OS CFAR processors in local detectors, is obtained. In the binary type, we analyzed various distributed detection schemes based on maximum, minimum, and summation rules in fusion center. In the fuzzy type, we consider the various distributed detectors based on algebraic product, algebraic sum, probabilistic OR, and Lukasiewicz t-conorm fuzzy rules in fusion center. The performance of the two types of distributed detectors is analyzed and compared in the homogenous and nonhomogenous situations, multiple targets, or clutter edge. The simulation results indicate the superiority and robust performance of fuzzy type in homogenous and non homogenous situations.

scholarly journals Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

2019 ◽  
Vol 11 (12) ◽  
pp. 1428 ◽  
Author(s):  
Yong Jia ◽  
Yong Guo ◽  
Chao Yan ◽  
Haoxuan Sheng ◽  
Guolong Cui ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Continuous Wave ◽  
Coefficient Matrix ◽  
Constant False Alarm Rate ◽  
Localization Algorithm ◽  
Cfar Detection ◽  
Stepped Frequency ◽  
The Cross ◽  
The One ◽  
Detection And Localization

This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm.

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.

Parameter Optimization of Decentralized OS-CFAR System Based Modified PSO Method

2012 ◽  
Vol 532-533 ◽  
pp. 881-886
Author(s):  
Pan Zhi Liu ◽  
Ruo Yu Pan ◽  
Guo Fang Guo
Keyword(s):  
Optimization Problem ◽  
Detection System ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Fusion Center ◽  
Threshold Values ◽  
Cfar Detection ◽  
Computation Technique ◽  
And Performance ◽  
Ordered Statistics

For decentralized ordered statistics (OS) constant false alarm ration (CFAR) detection system, the parameter estimation and performance analysis in complicated detection condition is a typical nonlinear optimization problem. Owing to the nonlinear property of distributed OS-CFAR detection system, it is seriously difficult to obtain optimal threshold values using some optimization method at the fusion center. This paper provides a novel solution based on an effective and flexible particle swarm optimization (PSO) algorithm. As a novel evolutionary computation technique, PSO has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. Using this approach, all system parameters can be optimized simultaneously. The simulation results show that the proposed approach can achieve effective performances with the above method.

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.

scholarly journals Bathtub-Shaped Failure Rate of Sensors for Distributed Detection and Fusion

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Junhai Luo ◽  
Tao Li
Keyword(s):  
Failure Rate ◽  
Fusion Rule ◽  
Distributed Detection ◽  
Fusion Center ◽  
Ratio Test ◽  
Failure Model ◽  
Sensor Failure ◽  
Robust Performance ◽  
Random Failure ◽  
Local Observation

We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensors which may or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the “bathtub curve”), which can be divided into the three following regions: initial failure period, random failure period, and wear-out failure period. Considering the possibility of the failed sensors which still work but in a bad situation, it is unreasonable to trust the data from these sensors. Based on the above situation, we bring in new characteristics to failed sensors. Each sensor quantizes its local observation into one bit of information which is sent to the FC for overall fusion because of power, communication, and bandwidth constraints. Under this sensor failure model, the Extension Log-likelihood Ratio Test (ELRT) rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures.

scholarly journals WEIGHTED AVERAGE POWER SIMILAR TESTS FOR STRUCTURAL CHANGE IN THE GAUSSIAN LINEAR REGRESSION MODEL

2008 ◽  
Vol 24 (5) ◽  
pp. 1277-1290
Author(s):  
Giovanni Forchini
Keyword(s):  
Structural Change ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Quadratic Forms ◽  
Average Power ◽  
Numerical Algorithms ◽  
Weighted Average ◽  
Test Statistics ◽  
Weighting Functions

Average exponential F tests for structural change in a Gaussian linear regression model and modifications thereof maximize a weighted average power that incorporates specific weighting functions to make the resulting test statistics simple. Generalizations of these tests involve the numerical evaluation of (potentially) complicated integrals. In this paper, we suggest a uniform Laplace approximation to evaluate weighted average power test statistics for which a simple closed form does not exist. We also show that a modification of the avg-F test is optimal under a very large class of weighting functions and can be written as a ratio of quadratic forms so that both its p-values and critical values are easy to calculate using numerical algorithms.

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