Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

1990/01/01 by I.S. Reed, X. Yu, Xinyi Yu · 1,806 citations
Engineering · Mathematics · #Advanced SAR Imaging Techniques #Algorithm #Artificial intelligence #Computer science #Constant false alarm rate #False alarm #Image (mathematics) #Infrared Target Detection Methodologies #Mathematics #Noise (video) #Pattern recognition (psychology) #Radar Systems and Signal Processing #Ratio test #SIGNAL (programming language) #Signal-to-noise ratio (imaging) #Statistics #Telecommunications

paper · doi:10.1109/29.60107

published in IEEE Transactions on Acoustics Speech and Signal Processing 38(10), 1760-1770 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1990/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

A constant false alarm rate (CFAR) detection algorithm (see J.Y. Chen and I.S. Reed, IEEE Trans. Aerosp. Electron. Syst., vol.AES-23, no.1, Jan. 1987) is generalized to a test which is able to detect the presence of known optical signal pattern which has nonnegligible unknown relative intensities in several signal-plus-noise bands or channels. This test and its statistics are analytically evaluated, and the signal-to-noise ratio (SNR) performance improvement is analyzed. Both theoretical and computer simulation results show that the SNR improvement factor of this algorithm using multiple band scenes over the single scene of maximum SNR can be substantial. The SNR gain of this detection algorithm is compared to the previously published one. It illustrates that the generalized SNR of the test using the full data array is always greater than that of using partial data array. The database used to simulate this adaptive CFAR test is obtained from actual image scenes.>

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