Trace-Norm Regularized Multi-Task Learning for Sea State Bias Estimation

• 作 者: ZHONG Guoqiang QU Jianzhang WA
• 机 构: DepartmentofComputerScienceandTechnology,DepartmentofPhysics,DepartmentofComputerScience
• 出 处: 中国海洋大学学报:英文版
• 发表日期: 2020年6期
• 关键词: sea state bias(SSB) radar altimeter geophysical data records(GDR) trace-norm mul
• 基金项目: This work was supported by the Major Project for New Generation of AI(No.2018AAA0100400),the National Natural Science Foundation of China(No.41706010),the Joint Fund of the Equipments Pre-Research and

Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only