TY - JOUR
T1 - Bayesian Interference Control CFAR Detector in Gamma-Distributed Background Using Discrete Truncated Gaussian Function
AU - Wang, Rui
AU - Shi, Mengxin
AU - Jiang, Qi
AU - Yan, Yujia
AU - Wang, Jiangtao
AU - Tian, Weiming
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - 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 article proposes a Gamma-distributed background Bayesian interference control 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. In addition, 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.
AB - 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 article proposes a Gamma-distributed background Bayesian interference control 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. In addition, 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.
KW - Bayesian interference control
KW - Gamma clutter
KW - constant false alarm rate (CFAR)
KW - discrete truncated gaussian function
UR - https://www.scopus.com/pages/publications/105027784834
U2 - 10.1109/TAES.2026.3653348
DO - 10.1109/TAES.2026.3653348
M3 - Article
AN - SCOPUS:105027784834
SN - 0018-9251
VL - 62
SP - 4813
EP - 4826
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
ER -