Variability index constant false alarm rate for marine target detection

Patel, Vikas; Madhukar, H; Ravichandran, Sujatha

doi:10.1109/spaces.2018.8316339

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…The VI CFAR, one of efficient CFAR algorithms, is the mostly accepted composite CFAR method is shown in figure 5. Dynamic selection of one of CFAR methods of mean level, on the basis of variability index and a mean ratio test is done by VI-CFAR for performing in robust manner especially for multiple targets environment [9]. Where, µ is mean and is variance of window, X is the mean of n cells in a leading or lagging half of reference window [9].…”

Section: Variability Index Cfar Methodsmentioning

confidence: 99%

“…Dynamic selection of one of CFAR methods of mean level, on the basis of variability index and a mean ratio test is done by VI-CFAR for performing in robust manner especially for multiple targets environment [9]. Where, µ is mean and is variance of window, X is the mean of n cells in a leading or lagging half of reference window [9]. Where Xi is the power of reference cell i.Variability of window is estimated by applying hypothesis test given by Eq.…”

Section: Variability Index Cfar Methodsmentioning

confidence: 99%

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Multiple targets detection in the marine environment using matlab

Satyanarayana²,

V.B.K.L³

2018

IJET

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This paper implements multiple targets detection against sea clutter by mathematically modeling target as a single point target and seaclutter as k-distribution model and observing the deterioration in the effective operation of radar using signal processing by matlab. A Variability Index (VI) algorithm is suggested for detection on the basis of constant false-alarm rate (CFAR) property even in heterogeneous conditions. Comparative analysis of VI-CFAR against various CFAR methods (CA, GO, SO, OS) for multiple targets detection in heterogeneous environment is done in this paper. Comparison and evaluation is done by means of the data which is simulated in MATLAB. The logical operation of the method is verified in heterogeneous condition.

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Section: Variability Index Cfar Methodsmentioning

confidence: 99%

Section: Variability Index Cfar Methodsmentioning

confidence: 99%

Multiple targets detection in the marine environment using matlab

Satyanarayana²,

V.B.K.L³

2018

IJET

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“…In order to make the detection algorithm able to adapt to the uniform clutter and non-uniform clutter background, the homogeneity of the clutter needs to be sensed. According to literatures [6] and [24], VI (variability index) and MR (Mean Ratio) detectors can be used to effectively detect the inhomogeneity of clutter.…”

Section: Window Control and Algorithm Determination Strategymentioning

confidence: 99%

Research on a New Comprehensive CFAR (Comp-CFAR) Processing Method

et al. 2019

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Since the clutter statistics of marine radar are non-stationary and difficult to ascertain, the constant false-alarm rate (CFAR) processor based on some clutter statistical characteristics is hard to obtain the CFAR performance. Especially, for clutter with long smearing effect characteristics, such as lognormal distribution, Pareto distribution, and K distribution, it is difficult to obtain CFAR characteristics using conventional CFAR processing techniques. The main consideration of this paper is to improve the robustness of CFAR, and the Comp-CFAR method is proposed according to the central limit theorem and the logarithmic compression principle of the signal. This method mainly includes clutter two-parameter logarithmic compression processing and accumulation of the magnitudes' average comprehensive CFAR processing. The experimental verification of CFAR characteristics and target detection performance with CFAR in four typical clutter environments shows that this method has better detection ability compared with the NCI-CFAR.INDEX TERMS Adaptive CFAR, clutter suppression, accumulation of the magnitudes, target detection.

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“…According to the homogeneity judgment of the leading and lagging reference windows, the VI-CFAR selects the detector from the CA-CFAR, GO-CFAR, and SO-CFAR. Some researchers applied the VI-CFAR to monostatic sonar system, which performs good detection property in reverberation edge and single interference environments [20,21] . Likely, we fully assess the homogeneity and consistency of the echo data from all the transmit-receive nodes.…”

Section: Introductionmentioning

confidence: 99%

Robust Centralized CFAR Detection for Multistatic Sonar Systems

Ding

2021

Chinese J of Electronics

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This paper proposes a novel centralized Constant false alarm ratio (CFAR) detector for multistatic sonar systems. The detector employs the idea of Variability index (VI) CFAR detection, to adaptively select the matched detection algorithm in diversified undersea environments. All the echo data from mutistatic sonar receivers are transmitted into the centralized fusion center. Firstly, the background statistics of reference cells from different nodes are analyzed. Then choose one appropriate centralized detection algorithm according to the background statistics, which refers to the centralized Cell averaging CFAR (CA‐CFAR), greatest of CFAR, order statistic CFAR detection algorithms. The performance of the proposed detector is analyzed by computer simulation and measured sonar data. The results show that, compared to the centralized CA‐CFAR detector, the introduced centralized detector achieves a better robustness in multiply heterogeneous undersea environments.

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Variability index constant false alarm rate for marine target detection

Cited by 9 publications

References 5 publications

Multiple targets detection in the marine environment using matlab

Multiple targets detection in the marine environment using matlab

Research on a New Comprehensive CFAR (Comp-CFAR) Processing Method

Robust Centralized CFAR Detection for Multistatic Sonar Systems

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