基于数据相关的多雷达融合成像相干配准研究

Research on multiple radar fusion imaging coherence compensation based on data correlation

针对多频带雷达融合成像中不同雷达信号间的相干配准问题,提出了一种新的基于雷达测量数据相关的相干配准方法。该方法在相干配准时不需对各雷达数据分别建模,相对于现有文献中采用的基于信号模型的相干配准法,降低算法复杂度的同时消除了建模误差,提高了相位参数估计的精度。对相干配准后的各雷达数据,通过超分辨和最优迭代方法建立全局信号模型,并对空白频带进行数据预测,得到整个频域范围的目标频率响应。仿真结果表明,该方法相干配准时相位参数的估计性能和最终的融合成像性能均优于现有的模型相关方法,且具有良好的抗噪性能。

To study the coherent compensation problem of multiband radar signal fusion imaging,a new data-based coherent compensation method is proposed to compensate for lack of mutual coherence among different radars.Compared with the existing model-based coherent compensation method,modeling of individual bands is not needed in the proposed method.Therefore the algorithm is simplified and the precision of phase parameters estimation is improved because of eliminating the modeling error.Then the super-resolution method and the linear least squares method are applied to obtain the poles of the global signal model and the amplitude coefficients of the scattering centers,and the optimized global signal model is established utilizing the nonlinear least square method.Simulation results show that the proposed scheme has better precision of phase parameters estimation and is superior to the original one under fusion imaging.Moreover,it has nicer anti-noise capability.