【摘要】 在强噪声背景下,基于时频联合分析的高频CW信号检测算法性能严重下降,同时标准Kalman滤波器对非平稳背景噪声下微弱高频CW信号也失效。针对此问题,本文提出了一种基于ARMA新息模型的自适应Kalman滤波器检测方法。该方法避免了标准Kalman滤波检测CW信号时需要确定系统噪声统计特性的问题。论文根据CW信号的状态空间随机信号模型,构造了ARMA新息模型,通过在线辨识MA模型参数来估计Kalman滤波增益,从而实现了CW信号的自适应跟踪滤波。仿真结果表明,该方法能够在强噪声背景下动态跟踪CW信号时域波形,且算法简单,实时性强,可用于指导高频CW电报自动接收设备的研制。更多还原

【Abstract】 The method of time-frequency transformation and classical Kalman filter for detecting high-frequency CW signal have a noticeable decline in performance with heavy and non-stationary background noise.A new adaptive Kalman filter based on ARMA innovation model is designed to detect weak high-frequency CW signal in strong Non-stationary noise environments.The method avoids the shortcomings of classical Kalman filter which requires precise statistical characteristic of noise in system.The ARMA innovation model of CW signals is constructed firstly,according to its state space random signal model.Then with the on-line identification of MA model parameters,the Kalman filter gain is estimated to implement the adpative Kalman filter of CW signals.Simulation studies show this method more efficient and simple in the detection of weak CW signal.The new method can be applied to develop automatic receiver of high-frequency CW telegraph signal.更多还原