多子带雷达信号融合噪声抑制方法

全极点模型对信号构建线性模型,通过对模型进行定阶和计算可以得到估计信号的各个参数,从而实现信号预测。子带融合之前需要进行相干处理,可以通过相干函数等方式解决。root-MUSIC算法在低信噪比环境中极点选择并不稳健,会导致模型在阶数上判断错误。为解决全极点模型方法中子带融合在信噪比低时模型阶数估计不准的问题,提出一种噪声抑制方法。对信号Hankel矩阵的主对角奇异值矩阵加权处理,以消除噪声分量,并利用整体前向预测矩阵得到整体频段极点值与极点幅值,由此估计出低信噪比时的多子带融合信号。结果表明所提方法在仿真环境下,在信噪比-20 dB至10 dB时,相较于传统极点模型方案具有更好的估计结果。

Noise suppression method for multi-subband radar signal fusion

All-pole model constructs a linear model of signal, and various parameters of the estimated signal can be obtained by fixing the order of the model and calculating it, so as to realize signal prediction. Before sub-band fusion, coherence processing is required and can be solved by means of coherence function and so on. Since the root-MUSIC algorithm is not robust in pole selection in a low signal-noise ratio environment, it will cause the model to judge the order incorrectly. In order to solve the problem that the order estimation of subband fusion is inaccurate when the signal-to-noise ratio is low in the all-pole model, a noise suppression method was proposed. The main diagonal singular value matrix of Hankel matrix was weighted to eliminate the noise component, and the overall forward prediction matrix was used to obtain the pole value and pole amplitude of the multi-subband fusion signal, so that the multi-subband fusion signal at low signal-to-noise ratio was estimated. The results show that the proposed method has better estimation results than the traditional pole-model scheme when the signal-to-noise ratio is -20 dB to 10 dB in simulation environment.