在非高斯马尔可夫噪声中的Robust检测

Robust Detection in Non-Gaussian Markov Noise

文中探讨了在仅已知噪声边缘分布族和相关系数的条件下非高斯马尔可夫噪声中确知信号的Robust检测问题。设噪声样本可表示为一阶AR过程:n_i=ρn_(i-1)+Y_i,(i=1,2,…,N),其中Y_i为独立同分布的随机序列。当p较小时,以检测器效验为性能指标并对ρ取一级和二级近似,分别得到了对应的有记忆和无记忆Robust检测器的结构。

Robust detection of known signal in non-Gaussian Markov noise is consid- ered under the assumptions that only marginal distribution class and dependent coefficient of noise samples are given, and the noise sequences can be expressed as a first-order AR process: n_i=ρn_(i-1)+Y_i (i=1, 2, …N), where {Y_i} is i·i·d· sequence.If ρ is smaller. the memory and memoryless robust detectors are respectively derived by minimax of the first-order and the second-order approximation of detection efficacy to ρ.