Bayesian Interference Control CFAR Detector in Gamma-Distributed Background Using Discrete Truncated Gaussian Function

Ensuring constant false alarm rate (CFAR) detection in complex non-Gaussian heterogeneous backgrounds poses a major challenge in low-altitude target monitoring. Heterogeneous Gamma clutter is a typical non-Gaussian heterogeneous background. Its heavy-tailed characteristics, together with masking effects from strong neighboring scatter interference, cause inaccurate background level estimation in conventional detectors. As a result, the detection performance degrades significantly. To overcome above detection difficulties, this paper proposes a Gamma-distributed background Bayesian interference control CFAR (GBIC-CFAR) detector. A standard CFAR detection procedure is developed using Bayesian methods, resulting in a Bayesian predictive inference model for Gamma clutter. Meanwhile, by constructing a reasonable predictive inference distribution to control interference, effective compensation for interference is achieved. Additionally, to address errors caused by inaccurate prior knowledge of interference in the Bayesian detector, a method for selecting interference prior probabilities based on discrete truncated Gaussian function is proposed. By adjusting the truncation threshold, the prior probabilities are optimized, improving the robustness of the proposed detector. Finally, the effectiveness of the proposed algorithm is validated through simulations and real data.