A Hybrid Wind Retrieval Method Based on Least Squares and Bayesian Estimation With Multiple Heterogeneous Sensors in Close Proximity

The least-squares (LS) method is commonly employed to retrieve the 3-D wind field by joint detection with multiple sensors, but it tends to suffer from ill-conditioning and reduced accuracy when the sensors are closely deployed and heterogeneous. This letter proposes a new method, denoted as TSVD-LSBE to address the ill-conditioning and sensor heterogeneity problem. First, the LS algorithm, regulated by the truncated singular value decomposition (TSVD), is utilized to get a preliminary estimation of the wind field. Then, the modified Bayesian estimation (BE), which is also regulated by TSVD and uses the modified LS result as the background wind, is employed to tune the preliminary estimation to a more accurate one. Simulation results demonstrate that the new method can enhance the retrieval accuracy by up to 50% in various wind field detection scenarios, surpassing both the modified-LS method and the local and global variational method (LGVM) based on single sensor measurement.