低信噪比下多频段雷达数据高精度相参配准

子带相参配准是多波段雷达数据融合技术首先要解决的问题,它直接关系到多波段数据融合的成败。基于数据相关与基于Prony模型的方法在弱噪声的条件下能取得比较理想的相参配准结果,但在噪声较强的条件下配准误差较大。本文分析了线性相位和固定相位对一维像的影响,在此基础上利用累加一维像的图像熵,提出了依据最小熵准则和非线性最小二乘拟合获得高精度相位参数估计的方法。仿真实验表明,本文所提出的方法在低信噪比条件下的参数估计精度优于现有的方法。

High precision coherent compensation for multiband radar data at low SNR

Coherent compensation among subbands is the first problem to be solved in multi-band radar data fusion, which is directly related to the success of the multi band data fusion. At present, there are two popular methods, one is based on the data correlation, and the other is based on the Prony model. Both of them can achieve satisfactory results at high signal to noise ratio (SNR), but not applicable in high clusters. This paper analyzes the impact of fixed phase and linear phase which caused the coherent phase mismatch on high radar range profile(HRRP), then proposes a highly accurate phase parameter estimation method according to the entropy minimization principle and nonlinear least-squares fit. Simulation results show that the method proposed can get a higher phase estimation accuracy in low SNR conditions.