Altimetric Parameter Estimation in Long Coherent Processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

Abstract

The along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based on a long CPI is proposed in this article. The long CPI is capable of achieving higher azimuthal resolution than that in the conventional altimeter with limited number of pulses. To conquer the problem of atmosphere turbulence and in accordance of motion deviation of the airborne platform, a subaperture phase gradient autofocusing framework is introduced to alleviate nonsystematic phase errors (NsPE). In this framework, NsPE is estimated and compensated in along-track so that a fully focused delayed Doppler map (DDM) can be guaranteed. Finally, the height parameter estimation is performed to the 1-D altimeter echoes after multilooking processing of DDM to improve the estimation accuracy. Given that the conventional retracking algorithm is sensitive to noise, which may degrade the estimation accuracy, a flexible Bayesian method is designed in a hierarchical manner for the SARAL retracking. The SARAL raw data are utilized in the experiment. The results of image entropy and RMSE demonstrate the effectiveness and superiority of the proposed algorithm both qualitatively and quantitatively.

Keywords

• Coherent processing interval (CPI)
• hierarchical Bayesian learning
• phase gradient autofocusing (PGA)
• synthetic aperture radar altimeter (SARAL)