微弱高频CW信号的自适应滤波

高频电报(CW)是强噪声背景下战术应急通信的主要工作方式，由于高频信道是典型的随参信道，不可能事先已知干扰噪声的统计特性。该文提出了一种基于ARMA新息模型的CW信号自适应Kalman滤波方法，以解决高斯背景下高频电报系统干扰噪声方差未知的问题。根据CW信号的时频域特征定义状态空间随机信号模型，构造ARMA新息模型，通过在线辨识新息模型参数来估计Kalman滤波增益，实现CW信号的自适应跟踪滤波。仿真结果表明，该方法能够有效估计微弱高频CW信号时域波形，算法可递推实现，实时性强。

Adaptive Filtering of Weak High-Frequency CW Telegraph Signal

Continuous wave (CW) telegraph is a crucial communication means for high-frequency tactical communication in emergencies. But there exists serious decline in high-frequency channel, thus the statistical properties of interference noise can not be known in advance. A new adaptive Kalman filter based on autoregressive moving average (ARMA) innovation model is proposed in this paper to detect weak high-frequency CW signal with unknown precise statistical variance of Gaussian noise in system. The state space random signal model of CW signal is firstly defined, by which the ARMA innovation model is constructed. Then by means of the on-line identification of ARMA model parameters, the Kalman filter gain is estimated to implement the adpative Kalman filtering of CW signal. Simulation studies show this method can dynamically track weak CW signal with unknown variance of Gaussian interference noise.