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基于最大概率估计和高斯-牛顿算法的大气相位校正模型的GB-SAR估计参数的新方法

Xiheng Li^1,2, Yu Liu^1,2

¹School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404130, China.

Sensors (Basel, Switzerland)

|September 14, 2024

概括

这项研究引入了一种用于地面合成光圈雷达 (GB-SAR) 的大气相位校正 (APC) 的新方法. 这种新的方法通过使用最大概率估计和高斯-牛顿算法来提高准确性,避免了常见的阶段解封错误.

科学领域:

遥感遥感遥感遥感
地质物理学地质物理学

背景情况:

大气相位误差显著影响地面合成光圈雷达 (GB-SAR) 的准确性.
复杂的大气相位屏幕 (APS) 需要强大的大气相位校正 (APC) 模型.
使用相解封和最小方形方法 (LSM) 的传统APC方法容易出现错误,特别是在相包裹永久散射器 (PSs) 中.

研究的目的:

开发一种用于估计大气相位校正 (APC) 模型参数的新方法.
通过克服传统阶段解封和LSM的局限性,提高APC的准确性.
在GB-SAR中为非线性远端和近端校正模型提供更可靠的参数估计.

主要方法:

最大概率估计 (MLE) 用于制定参数估计的客观函数.
高斯-牛顿算法用于对象函数参数的代估计.
马修斯和戴维斯算法优化高斯-牛顿算法以提高准确性.
蒙特卡洛模拟用于绩效评估.

主要成果:

拟议的方法成功地估计了APC模型参数,而无需进行阶段解封.
最大概率估计为非线性校正模型提供了合适的客观函数.
优化的高斯-牛顿算法提高了参数估计的准确性.
蒙特卡洛模拟证实了拟议方法的可行性和优越性.

结论:

基于MLE和高斯-牛顿算法的新方法为GB-SAR中APC提供了优越的替代方案.
这种方法有效地减轻了与传统阶段解封技术相关的错误.
开发的方法证明了复杂的大气相屏校正的提高准确性和可行性.

关键词:

高斯 - 牛顿算法高斯 - 牛顿算法马修斯和戴维斯的算法大气相位校正 (APC)基于地面的合成光圈雷达 (GB-SAR)最大的概率估计 (MLE)

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