Development of a polarimetric CFAR detector using Markov Chains

The paper proposes a novel constant false alarm rate (CFAR) detector using Markov chain models, an innovative new technique that will utilize the finer resolution of RADARSAT-2 to yield improved detection performance for higher-resolution objects. The Markov chain based CFAR detector extends traditi...

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Bibliographic Details
Main Authors: Chuhong Fei, Anastassopoulos, V., Ting Liu, Lampropoulos, G.A., Murnaghan, K., Sabry, R.
Format: Conference Proceeding
Language:English
Subjects:
Background noise
CFAR detection
Clutter
Detectors
Hidden Markov models
Hyperspectral sensors
Laboratories
Markov chains
Object detection
polarimetric SAR
Probability density function
Radar detection
Reflection
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Summary:The paper proposes a novel constant false alarm rate (CFAR) detector using Markov chain models, an innovative new technique that will utilize the finer resolution of RADARSAT-2 to yield improved detection performance for higher-resolution objects. The Markov chain based CFAR detector extends traditional PDF based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and PDF information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Our both analytical and experimental results both show that the new Markov chain CFAR detector can improve the conventional PDF CFAR detector about 30% in terms of detection probability gain and about 2.84 dB in terms of signal-to-clutter ratio gain.
ISSN:1097-5659
2375-5318
DOI:10.1109/RADAR.2008.4721036