Elevation Estimation for Airborne Synthetic Aperture Radar Altimetry Based on Parameterized Bayesian Learning

YANG Lei; ZHOU Honghao; HUANG Bo; LIAO Xianhua; XIA Yabo

doi:10.11999/JEIT220322

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1254-1264

YANG Lei, ZHOU Honghao, HUANG Bo, LIAO Xianhua, XIA Yabo. Elevation Estimation for Airborne Synthetic Aperture Radar Altimetry Based on Parameterized Bayesian Learning[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1254-1264. doi: 10.11999/JEIT220322

Citation:

YANG Lei, ZHOU Honghao, HUANG Bo, LIAO Xianhua, XIA Yabo. Elevation Estimation for Airborne Synthetic Aperture Radar Altimetry Based on Parameterized Bayesian Learning[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1254-1264. doi: 10.11999/JEIT220322

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Elevation Estimation for Airborne Synthetic Aperture Radar Altimetry Based on Parameterized Bayesian Learning

doi: 10.11999/JEIT220322

1.
Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
2.
Institute of Electronic Engineering, Chinese Academy of Engineering Physics, Mianyang 621999, China

Funds: The National Natural Science Foundation of China (61601470), The Natural Science Foundation of Tianjin (16JCYBJC41200)

Received Date: 2022-03-25
Rev Recd Date: 2022-05-24

Available Online: 2022-06-01

Publish Date: 2023-04-10

Abstract

Abstract

Airborne Synthetic Aperture Radar Altimeter (SARA) is capable of exploiting the high-resolution in along-track, which has been attracted wide concerns. However, the existing re-tracking methods are mostly based on the least square operator. The performance of estimation accuracy and noise suppression of the operator are limited due to neglect of noise factors and accordance over-fitting problem. In this paper, Parameterized Retracking Bayes (PR-Bayes) algorithm is proposed under the framework of Bayesian machine learning. By introducing a prior probability model of the terrain scene, and combining with model-driven machine learning method, the elevation information of re-tracking with reliable estimation of the target can be achieved. The problem about over-fitting can be alleviated effectively. In this algorithm, Brown Model (BM) is used to recover complicated model parameters of SARA echo. Then, Hamilton Monte Carlo (HMC) statistical sampler is designed to estimate the terrain height of the scene with a high accuracy and reliable confidence. The accuracy and validity of this algorithm are verified by point target simulation and semi-physical simulation based on DEM respectively, and the practicability is proved by the airborne raw SARA data.
- Synthetic Aperture Radar Altimeter(SARA),
- Bayesian learning,
- Retracking,
- Brown Model (BM),
- Hamilton Monte Carlo(HMC) sampling

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References(21)

References

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