Detection of small targets in clutter modeled as a Gaussian mixture or a hidden Markov process

David W. J. Stein

doi:10.1117/12.283970

29 October 1997 Detection of small targets in clutter modeled as a Gaussian mixture or a hidden Markov process

David W. J. Stein

Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.283970
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Sea clutter amplitude is often modeled as a compound random variable Z equals AX, where A is a positive valued random variable and X has a Rayleigh distribution. The K, class A, and discrete Rayleigh mixture distributions can be derived from these assumptions. Moreover, successive values of A may be correlated. If A is modeled as a finite Markov process, Z is described by a hidden Markov model (HMM). The applicability of Rayleigh mixture and hidden Markov models to RADAR sea clutter is demonstrated empirically. Amplitude only and phase coherent detection statistics are derived from these models using locally optimal and likelihood ratio techniques. Robust implementations of the locally optimal processor based on the Rayleigh mixture model have been developed, and empirical ROC curves demonstrate performance improvement of up to 9 dB in comparison with a CFAR detector for small targets in sea clutter. In a test case, the locally optimal hidden Markov detector is then shown to offer an additional 3 dB over the Gaussian mixture detector. Further examples compare the amplitude and phase coherent hidden Markov detectors with CFAR and Doppler processors.

Citation Download Citation

David W. J. Stein "Detection of small targets in clutter modeled as a Gaussian mixture or a hidden Markov process", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); https://doi.org/10.1117/12.283970

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