非高斯背景下的自适应限幅检测研究

针对非高斯背景下局部最优检测（Locally Optimal Detector，LOD）结构复杂、稳健性弱的问题，对传统的限幅器进行改进，提出了一种自适应限幅检测器（Adaptive Limiter Detector，ALMD）.首先对弱信号的检测性能进行了系统研究，然后依据混合高斯模型对非高斯背景建模，在此基础上得到了限幅阈值与限幅检测性能之间的解析表达式，最后通过推导确定了最佳限幅阈值，明显提高了检测性能.仿真结果表明ALMD与LOD性能接近，但结构简单，性能稳健，适应性更强.

Adaptive Limiter Detection in Non-Gaussian Noise Background

Aiming at the problem that the local optimal detector(LOD) has complex detection structure and poor robustness for signal detection in Non-Gaussian noise background,an adaptive limiter detector (ALMD) is proposed based on the improvement of traditional limiter.Firstly,the detection performance of weak signal is systematically studied.Then the non-Gaussian background is modeled by the mixed Gaussian model.On this basis,the analytical expression between the clipping threshold and the detection performance is obtained.Finally,the optimal threshold is determined which leads to a significantly improvement of the detection performance.Simulation results shows that the ALMD has similar performance compared to LOD but provide relatively efficient and robustness.