Weighted Likelihood CFAR Detection for Weibull Background

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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Survey on Fast CFAR Detection Algorithms for SAR Image Targets

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.01885 ◽

2012 ◽

Vol 38 (12) ◽

pp. 1885 ◽

Cited By ~ 4

Author(s):

Ming-Bo ZHAO ◽

Jun HE ◽

Qiang FU

Keyword(s):

Sar Image ◽

Cfar Detection ◽

Detection Algorithms

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CFAR Detection Based on the Nonlocal Low-Rank and Sparsity-Driven Laplacian Regularization for HFSWR

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2021.3090930 ◽

2021 ◽

pp. 1-1

Author(s):

Xinyang Wang ◽

Yang Li ◽

Ning Zhang ◽

Qingxiang Zhang

Keyword(s):

Low Rank ◽

Cfar Detection ◽

Laplacian Regularization

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Robust estimation for multivariate wrapped models

METRON ◽

10.1007/s40300-021-00214-9 ◽

2021 ◽

Author(s):

Giovanni Saraceno ◽

Claudio Agostinelli ◽

Luca Greco

Keyword(s):

Robust Estimation ◽

Numerical Study ◽

Real Data ◽

Likelihood Method ◽

Weighted Likelihood ◽

Finite Sample ◽

Pearson Residuals ◽

Data Points ◽

Wrapped Distributions ◽

Standard Techniques

AbstractA weighted likelihood technique for robust estimation of multivariate Wrapped distributions of data points scattered on a $$p-$$ p - dimensional torus is proposed. The occurrence of outliers in the sample at hand can badly compromise inference for standard techniques such as maximum likelihood method. Therefore, there is the need to handle such model inadequacies in the fitting process by a robust technique and an effective downweighting of observations not following the assumed model. Furthermore, the employ of a robust method could help in situations of hidden and unexpected substructures in the data. Here, it is suggested to build a set of data-dependent weights based on the Pearson residuals and solve the corresponding weighted likelihood estimating equations. In particular, robust estimation is carried out by using a Classification EM algorithm whose M-step is enhanced by the computation of weights based on current parameters’ values. The finite sample behavior of the proposed method has been investigated by a Monte Carlo numerical study and real data examples.

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

Remote Sensing ◽

10.3390/rs11121428 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1428 ◽

Cited By ~ 2

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.

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Bayesian Quickest Detection Problems for Some Diffusion Processes

Advances in Applied Probability ◽

10.1239/aap/1363354107 ◽

2013 ◽

Vol 45 (1) ◽

pp. 164-185 ◽

Cited By ~ 8

Author(s):

Pavel V. Gapeev ◽

Albert N. Shiryaev

Keyword(s):

Diffusion Processes ◽

Free Boundary Problems ◽

Three Dimensional ◽

Parabolic Type ◽

Value Functions ◽

Weighted Likelihood ◽

Likelihood Ratios ◽

Boundary Problems ◽

Detection Delay ◽

Penalty Costs

We study the Bayesian problems of detecting a change in the drift rate of an observable diffusion process with linear and exponential penalty costs for a detection delay. The optimal times of alarms are found as the first times at which the weighted likelihood ratios hit stochastic boundaries depending on the current observations. The proof is based on the reduction of the initial problems into appropriate three-dimensional optimal stopping problems and the analysis of the associated parabolic-type free-boundary problems. We provide closed-form estimates for the value functions and the boundaries, under certain nontrivial relations between the coefficients of the observable diffusion.

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