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2018•2018 IEEE 5th International Congress on Information Science and Technology (CiSt)Requires access

The Performance of Decentralized CFAR Detection Using Biogeography Based Optimization

Amel Gouri, Amar Mezache, Houcine Oudira

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Abstract

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian cluter using Biogeography Based Optimization (BBO) technique is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection.

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What this paper is about

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian cluter using Biogeography Based Optimization (BBO) technique is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection.

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Available abstract

In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian cluter using Biogeography Based Optimization (BBO) technique is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection.

Key concepts: Constant false alarm rate, Statistical power, Detector, False alarm, Computer science, Gaussian, Algorithm, Pattern recognition (psychology)

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