在量化阵列框架下的对称Alpha稳定信号检测的预处理方法

Preprocessing Method of Symmetric Alpha-Stable Signal Detection Based on Quantizer-Array Framework

本文主要研究了淹没在对称α稳态噪声下的信号相关检测的最优和次优的预测处理方法。使用量化阵列模型的等价处理函数和对相关运算的高斯近似,通过最大化相关器的输出信噪比,建立了约束泛函优化问题。由于量化阵列的泛函优化问题很难得到解析解,本文将预处理函数离散化,并证明离散后的优化问题是凸二次规划问题,从而可通过凸优化的方法求解。本文提出了一种基于排序方法的自适应门限的软限幅检测器,相比现有的检测器,仅仅需要估计噪声参数α。仿真结果表明,提出的量化阵列系统等价的预处理函数逼近最大似然检测器,提出的软限幅检测器达到了近似最优的性能,有利于实时处理α稳态信号。

In this paper,the optimal and suboptimal nonlinear processing for correlation-based signal detection is addressed in symmetric alpha-stable noise.By the equivalent preprocessing function of quantizer-array and Gaussian approximation of the correlator’s output,a constrained functional optimization problem is established by maximizing the correlator output signal-to-noise ratio.However,it is hard to get the analytical solution of this problem.We further apply finite discretization to this functional optimization problem,and prove that the approximation problem is a convex quadratic programming problem.Therefore,the resulting problem can be solved by convex optimization.We propose a novel adaptive threshold for the soft limiter detector based on sorting,which only need to estimate alpha-stable noise parameterαwhen comparing with other detectors.The proposed equivalent preprocessing function of quantizer-array approximates the maximum likelihood detector.Simulation results show that the proposed soft limiter achieves near-optimal performance,benefitting the real-time processing of symmetric alpha-stable signal.