A threshold factor approach method for CFAR detector based on stochastic particle swarm optimization

A threshold factor approach method for CFAR detector based on stochastic particle swarm optimization

Based on the perfect properties of stochastic particle swarm optimization (SPSO), such as the property of robust and quick convergence, a new scheme is applied to estimate scaling factor for radar constant false alarm rate (CFAR) detectors. Owing to few constraints, it can estimate scaling factor fo...

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Hauptverfasser: Liu, Panzhi, Han, Chongzhao, Jie, Jing
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Sprache:eng
Schlagworte:
Artificial neural networks
CFAR detector
Clutter
Detectors
Estimation
Particle swarm optimization
Radar
Radar detection
Scaling factor
stochastic particle swarm optimization (SPSO)
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Han, Chongzhao
Jie, Jing
description Based on the perfect properties of stochastic particle swarm optimization (SPSO), such as the property of robust and quick convergence, a new scheme is applied to estimate scaling factor for radar constant false alarm rate (CFAR) detectors. Owing to few constraints, it can estimate scaling factor for single radar as well as radar netting system. The numerical results indicate that the particle swarm optimizer has been found to be accuracy and fast in searching the threshold factor T of CFAR detector under any designed probability of false alarm.
doi_str_mv 10.1109/IJCNN.2008.4634127
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2161-4407
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial neural networks
CFAR detector
Clutter
Detectors
Estimation
Particle swarm optimization
Radar
Radar detection
Scaling factor
stochastic particle swarm optimization (SPSO)
title A threshold factor approach method for CFAR detector based on stochastic particle swarm optimization
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