A Robust Track Estimation Method for Airborne SAR Based on Weak Navigation Information and Additional Envelope Errors

Author:

Gao Ming123,Qiu Xiaolan145ORCID,Cheng Yao45,Chen Min123,Ding Chibiao123

Affiliation:

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

2. Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Chinese Academy of Sciences, Beijing 100190, China

3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

4. Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215124, China

5. Suzhou Aerospace Information Research Institute, Suzhou 215124, China

Abstract

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm.

Funder

National Key R&D Program of China

Publisher

MDPI AG

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