基于PHAF的多普勒参数估计

在通常情况下,多普勒参数是影响SAR成像质量的主要因素.目前,估计多普勒参数的算法主要有Mapdrift、相位梯度自聚焦以及对比度最优自聚焦等自聚焦算法,这些算法有一个共同的缺点,不能估计并补偿高阶多普勒参数.该文通过基于乘积型高阶模糊度函数(Product High-order Ambiguity Function,PHAF)算法来估计多普勒参数的新方法,该方法无需利用惯导数据预先计算多普勒调频斜率初值,可与杂波锁定并行完成,并且具有估计高阶多普勒参数的能力.仿真实验比较了PHAF和MapDrift分别在小信噪比,存在高阶误差时的自聚焦能力.结果说明该算法计算量小、鲁棒性强、估计精度高,在小信噪比情况下仍可得到较准确的估计结果.最后给出的成像结果说明该文提出的算法能够大大改善成像分辨率.

Doppler Parameter Estimation Based on Product High-Order Ambiguity Function

In general, Doppler parameters are the main reason that leads to degrading of the SAR (Synthetic Aperture Radar) imaging quality. Now the algorithms to estimate Doppler parameters mainly are Mapdrift, Phase Gradient Autofocus (PGA) algorithms and so on. The drawback of these algorithms lie in the fact that the high-order Doppler parameter can not be estimated, and need iteration during estimating. In this paper Product High-order Ambiguity Function (PHAF) is introduced to estimate the Doppler parameter in synthetic aperture radar. The new algorithm, which has the ability to estimate high order parameters, doesn’t need any initial information on Doppler rate and it can be completed with clutter lock at the same time. The algorithm based on PHAF is presented and analyzed in detail. The autofocus result is compared between PHAF and MapDrift under the condition of low Signal Noise Ratio (SNB) and with the existence of high order phase errors. It shows that PHAF is faster, more robust and accurate; meanwhile, exact result is available when SNB is low, and finally the imaging results indicate that the PHAF can improve resolution of SAR image greatly.