SαS分布下的累积分布检测器

Cumulative distribution detector under SαS distribution

针对对称α稳定(symmetricαstable,SαS)分布背景下局部最优检测(locally optimal detector,LOD)结构复杂、难以进行恒虚警处理的问题,提出一种自适应累积分布检测器(cumulative distribution detector,CDD)。该检测器首先利用累积分布函数对接收信号进行非线性变换,然后再进行匹配滤波处理。根据双参数柯西-高斯混合(bi-parameter Cauchy-Gaussian mixture,BCGM)模型对SαS分布进行近似,在此基础上对CDD的检测性能进行了系统分析,证明了其恒虚警特性,并通过Sigmoid函数对该检测器进一步简化得到了近似累积分布检测器(near CDD,NCDD)。理论和仿真结果都表明,当α较大时,NCDD性能接近LOD,但结构更加简单,更易于恒虚警处理。

Aiming at the problem that the locally optimal detector(LOD)has complex structure and is difficult to perform constant false alarm processing under the background of symmetricαstable(SαS)distribution,an adaptive cumulative distribution detector(CDD)is proposed.The detector performs nonlinear transformation on the received signal based on the cumulative distribution function,and then performs matched filtering processing.The SαS distribution is approximated according to the bi-parameter Cauchy-Gaussian mixture model(BCGM),and based on this,the detection performance of CDD is systematically analyzed,and its constant false alarm characteristics is proved.The detector is further simplified by the sigmoid function,and an near CDD(NCDD)is obtained.Both theoretical and simulation results show that NCDD has similar performance with LOD when the value ofαis large,but the structure is simpler and it is easier to deal with the constant false alarm ratio.