为了提高紫外探测系统性能, 研究具有高灵敏性紫外目标探测弱信号处理方法是关键问题之一。首先, 在阐述紫外目标探测原理的基础上, 分析紫外目标辐射特性。其次, 研究自适应噪声抵消信号处理的一般方法, 以及基于最小均方误差 LMS准则、递推最小二乘 RLS准则和线性神经网络 ADALINE的三种具体的自适应噪声抵消算法。再次, 提出采用功率信噪比来衡量滤波算法的性能。最后, 通过仿真计算比较分析这三种算法的滤波效果。结果表明：采用 LMS和 RLS算法信噪比提高约 12.5 dB, 且 LMS算法比 RLS算法略优, 而采用 ADALINE算法信噪比至少改善 26.6 dB, 可实现高性能滤波。对于紫外目标探测弱信号处理方法的发展与深入研究具有一定的作用和意义。

In order to increase performance of the ultraviolet (UV) detection system, the research of high sensitive UV target detection weak signal process is a key issue. Firstly, based on expounding working principle of UV detection system, this paper analyzes the radiation characteristics of the UV target. Then, general methods of adaptive noise canceling and three special methods, adaptive noise counteraction based on the basic theories and algorithmic character of the least mean square error rule adaptive noise canceling, recursive least square rule and linear neural network, are studied in this paper. Signal-to-noise ratio was employed to weigh the merit of the signal. Moreover, three methods are compared and analyzed. The results show that the signal-to-noise ratio improved about 12.5 dB by LMS algorithm and RLS algorithm. The LMS algorithm is slightly better than the RLS algorithm. But, the algorithm of adaline is improved at least 26.6 dB, which can be used to implement a high-performance filter. This paper provides certain theoretical guidance to the further research and development of the UV target detection weak signal process.