基于多站数据融合的参数精估计方法

针对侦察设备处于星载SAR副瓣照射范围，从而导致截获信号湮没于强噪声背景这个问题，本文提出一种基于多站接收机之间的数据融合方法。在信号形式未知的情况下，通过此方法可以检测出淹没在噪声中的微弱信号，进行信号的分类和时频域参数的精估计。首先，将参考接收机与其他接收机之间进行互相关处理，得到峰值信息，根据峰值信息的位置得到信号与参考信号之间的延迟位置，进行延迟校准；其次，各个接收机分别进行粗步长的分数阶傅里叶变换（Fractional Fourier Transform，FrFT），记录峰值信息为精估计做准备，根据峰值角度和分数阶傅里叶反变换恢复出原始信号；最后，判定是否存在信号，若信号存在实现多站原始信号功率比的加性融合，根据多站峰值信息限定旋转角度范围，采用精步长的分数阶傅里叶变换估计出调频率和中心频率；利用联合互相关谱实现信号能量的累积，采用自适应门线和边界波谷连续取小方法，找到信号存续状态中的左右边界，估计出带宽和中心频率，计算脉宽，实现时频域信号的精估计。仿真实验表明：该方法可以在低信噪比的高斯白噪声和有色噪声背景下，对线性调频信号（Chirp）的时频参数进行有效的精估计。

Precise Parameter Estimation Method Based on Multi-receivers Data Fusion

Regarding the issue of detection equipment being within the range of spaceborne SAR sidelobe, which causes the signal to be lost in the strong noise background, a method based on data fusion between multi-platform receivers is proposed. Without knowing the signal form, the weak signals submerged in noise can be detected, and the signal can be classified and accurately estimated. Firstly, the reference receiver and other receivers are cross-correlated to obtain the peak information, and the delay position between the signals and the reference signal is obtained according to the position of the peak information, in order to perform delay calibration. Secondly, each receiver performs coarse step FrFT filtering, records peak information for precise estimation, and restores the original signal based on the peak angle and the inverse FrFT. Finally, it is determined whether there is a signal. If the signal is achieved, a new signal will be formed by the fusion of power ratio of multi-platform receivers'' original signal. The rotation angle ranges are limited based on the peak information of multiple stations, and the precise step FrFT is used to estimate the chirp rate and central frequency. The joint cross-correlation spectrum analysis is used to realize the accumulation of signal energy, and the left and right boundaries in the signal persistence state are found by using the method of continuously minimizing the boundary valley. The bandwidth and central frequency are accurately estimated, and then calculate the pulse width. The simulation results show that this method can accurately estimate the parameters of the time-frequency domain of Chirp in the background of Gaussian white noise and colored noise with low noise.