Locally optimal detector design in impulsive noise with unknown distribution

• Main Authors: Zhongtao Luo, Peng Lu, Gang Zhang
• Format: Article
• Language: English
• Published: SpringerOpen 2018-06-01
• Series: EURASIP Journal on Advances in Signal Processing
• Subjects: Locally optimal detector; ZMNL function; Non-Gaussian distribution; Polynomial fitting
• Online Access: http://link.springer.com/article/10.1186/s13634-018-0560-x

Abstract This paper designs the locally optimal detector (LOD) in additive white impulsive noise with unknown distribution. Unlike traditional LODs derived from a known or approximated noise probability density function (PDF), the LOD proposed in this paper is achieved by designing the zero-memory non-linearity (ZMNL) function based on real data. After the PDF estimation in a nonparametric way by a kernel method, the ZMNL function is designed as a piecewise differentiable function consisting of a polynomial function and inverse proportional functions. Then, we analyze the detection performance and develop the constant false alarm ratio technique. Simulation results show that the LOD design is near-optimal in α-stable noise and the optimal in real atmospheric data, compared with the maximum likelihood detector of α-stable distribution.