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A Stationary Gaussian Process-based Radar Detector in Complex Gaussian Colored Noise

Haihua Xie,Jia Zhao

DIGITAL SIGNAL PROCESSING(2025)

Nanchang Inst Technol

Cited 0|Views3
Abstract
In modern radar systems, maintaining a constant false alarm rate (CFAR) is crucial for automatic target detection. However, in scenarios involving colored noise, the correlation between adjacent samples can adversely affect the accurate prediction of the background level, leading to a significant degradation in CFAR performance. To address this issue, this article employs a stationary Gaussian processes method to develop a radar detector for complex Gaussian colored noise, named sGP detector. The sGP detector treats the cell under test (CUT) and its adjacent cells as a Gaussian process. Maximum likelihood estimation is utilized to determine the hyper-parameters based on the samples from the reference cell. Subsequently, the posterior distribution of the CUT regression results is derived using the prior mean and kernel function, enabling the estimation of the background clutter level of the CUT. Consequently, an exact expression for the probability of false alarm (PFA) can be derived by integrating the distribution of the CUT, leading to the establishment of a decision rule that maintains the constant false alarm rate property. Simulation results validate the proposed detector's capability to function effectively under Gaussian colored noise. Furthermore, when compared to five existing CFAR detectors, the proposed detector demonstrates superior detection performance under typical reference window sizes and desired false alarm rate conditions.
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Key words
Gaussian colored noise,Stationary Gaussian process,Constant false alarm rate,Radar detection
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