机载合成孔径雷达高度计高程参数贝叶斯估计  

Elevation Estimation for Airborne Synthetic Aperture Radar Altimetry Based on Parameterized Bayesian Learning

作　　者：杨磊 周弘昊 黄博 廖仙华 夏亚波 YANG Lei;ZHOU Honghao;HUANG Bo;LIAO Xianhua;XIA Yabo(Tianjin Key Laboratory for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China;Institute of Electronic Engineering,Chinese Academy of Engineering Physics,Mianyang 621999,China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300 [2]中国工程物理研究院电子工程研究所,绵阳621999

出　　处：《电子与信息学报》2023年第4期1254-1264,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61601470);天津市自然科学基金(16JCYBJC41200)。

摘　　要：机载合成孔径雷达高度计(SARA)由于具有高航向分辨率,因此受到广泛关注。然而,现有的SARA地面高程重跟踪方法多基于最小二乘算子,高程参数估计精度和算法抑噪性能均存在上限,容易造成高程参数估计结果过拟合,对复杂高程变化适应能力有限。为此,该文提出一种基于参数化贝叶斯统计学习方法的机载SARA重跟踪算法(PR-Bayes)。通过引入目标场景地形先验概率模型,并结合模型驱动机器学习方法,可实现对目标高程信息重跟踪可信估计,从而有效避免估计参数过拟合问题。该算法基于布朗模型(BM)对SARA回波进行复杂模型参数反演,并设计哈密顿蒙特卡洛(HMC)统计采样器,实现对目标场景地形高度的参数估计。基于该文所提算法,分别通过点目标模拟和DEM半实物模拟对该算法进行有效性验证及高程参数估计精度验证,并通过实测数据验证该算法的实用性。Airborne Synthetic Aperture Radar Altimeter(SARA)is capable of exploiting the high-resolution in along-track,which has been attracted wide concerns.However,the existing re-tracking methods are mostly based on the least square operator.The performance of estimation accuracy and noise suppression of the operator are limited due to neglect of noise factors and accordance over-fitting problem.In this paper,Parameterized Retracking Bayes(PR-Bayes)algorithm is proposed under the framework of Bayesian machine learning.By introducing a prior probability model of the terrain scene,and combining with model-driven machine learning method,the elevation information of re-tracking with reliable estimation of the target can be achieved.The problem about over-fitting can be alleviated effectively.In this algorithm,Brown Model(BM)is used to recover complicated model parameters of SARA echo.Then,Hamilton Monte Carlo(HMC)statistical sampler is designed to estimate the terrain height of the scene with a high accuracy and reliable confidence.The accuracy and validity of this algorithm are verified by point target simulation and semi-physical simulation based on DEM respectively,and the practicability is proved by the airborne raw SARA data.

关 键 词：合成孔径雷达高度计 贝叶斯学习 重跟踪 布朗模型 哈密顿蒙特卡洛采样 

分 类 号：TN953[电子电信—信号与信息处理]