改进的扩展卡尔曼滤波相位解缠算法Improved extended Kalman filter phase unwrapping algorithm
许华夏,谢先明,谢家朝,曾庆宁
摘要(Abstract)：
针对扩展卡尔曼相位解缠(EKFPU)算法对低信噪比干涉图相位解缠精度不高的缺点,该文提出一种将径向基函数(RBF)神经网络和扩展卡尔曼滤波(EKF)算法相结合的干涉合成孔径雷达(InSAR)相位解缠算法。利用RBF神经网络的自适应调整能力和非线性拟合能力对EKF的结果进行调整、补偿,得到高精度的相位解缠结果;结合路径跟踪策略,利用质量图去引导EKF相位解缠,避免穿过误差较大的区域,进一步提高解缠的精度。通过对模拟数据和实测数据的仿真实验,验证了该算法的有效性。
关键词(KeyWords)： 相位解缠;扩展卡尔曼滤波;径向基神经网络;路径跟踪
基金项目(Foundation): 国家自然科学基金项目(61461011);; 广西自然科学基金项目(2014GXNSFBA118273);; 广西无线宽带通信与信号处理重点实验室2014—2015年主任基金项目(GXKL061503);; 认知无线电教育部重点实验室主任基金项目
作者(Author): 许华夏,谢先明,谢家朝,曾庆宁
DOI: 10.16251/j.cnki.1009-2307.2018.10.003
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