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机载合成孔径雷达高度计高程参数贝叶斯估计

杨磊 周弘昊 黄博 廖仙华 夏亚波

杨磊, 周弘昊, 黄博, 廖仙华, 夏亚波. 机载合成孔径雷达高度计高程参数贝叶斯估计[J]. 电子与信息学报, 2023, 45(4): 1254-1264. doi: 10.11999/JEIT220322
引用本文: 杨磊, 周弘昊, 黄博, 廖仙华, 夏亚波. 机载合成孔径雷达高度计高程参数贝叶斯估计[J]. 电子与信息学报, 2023, 45(4): 1254-1264. doi: 10.11999/JEIT220322
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

机载合成孔径雷达高度计高程参数贝叶斯估计

doi: 10.11999/JEIT220322
基金项目: 国家自然科学基金(61601470),天津市自然科学基金(16JCYBJC41200)
详细信息
    作者简介:

    杨磊:男,副教授,研究方向为高分辨SAR成像及机器学习理论应用

    周弘昊:男,硕士生,研究方向为机载雷达高度表系统及信号处理

    黄博:男,博士生,研究方向为雷达高度表系统及信号处理

    廖仙华:男,硕士生,研究方向为高分辨SAR成像及统计采样技术应用

    夏亚波:男,硕士生,研究方向为高分辨SAR成像及统计采样技术应用

    通讯作者:

    黄博 vick123y@163.com

  • 中图分类号: TN953

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

Funds: The National Natural Science Foundation of China (61601470), The Natural Science Foundation of Tianjin (16JCYBJC41200)
  • 摘要: 机载合成孔径雷达高度计(SARA)由于具有高航向分辨率,因此受到广泛关注。然而,现有的SARA地面高程重跟踪方法多基于最小二乘算子,高程参数估计精度和算法抑噪性能均存在上限,容易造成高程参数估计结果过拟合,对复杂高程变化适应能力有限。为此,该文提出一种基于参数化贝叶斯统计学习方法的机载SARA重跟踪算法(PR-Bayes)。通过引入目标场景地形先验概率模型,并结合模型驱动机器学习方法,可实现对目标高程信息重跟踪可信估计,从而有效避免估计参数过拟合问题。该算法基于布朗模型(BM)对SARA回波进行复杂模型参数反演,并设计哈密顿蒙特卡洛(HMC)统计采样器,实现对目标场景地形高度的参数估计。基于该文所提算法,分别通过点目标模拟和DEM半实物模拟对该算法进行有效性验证及高程参数估计精度验证,并通过实测数据验证该算法的实用性。
  • 图  1  合成孔径雷达高度计(SARA)工作原理示意图

    图  2  2维延时多普勒像(DDM)及点目标响应函数

    图  3  贝叶斯分层模型DAG

    图  4  本文所提PR-Bayes算法运算流程

    图  5  点目标估计结果

    图  6  蒙特卡洛模拟实验结果

    图  7  DEM半实物模拟仿真实验结果

    图  8  实测数据实验结果

    表  1  点目标仿真雷达参数

    雷达参数数值雷达参数数值雷达参数数值
    雷达载频9.6 GHz采样频率300 MHz波长3.13 cm
    脉冲宽度4 μs天线孔径0.4 m飞行高度1000 m
    信号带宽200 MHz脉冲重复频率2000 Hz载机速度60 m/s
    下载: 导出CSV

    表  2  点目标仿真实验结果

    噪声(dB)真值LS估计值PR-Bayes估计值精度提升(%)
    无噪声50.4050.0450.040.55
    050.4051.0250.0850.25
    -1050.4051.4949.7944.23
    下载: 导出CSV

    表  3  半实物仿真数据重跟踪结果定量分析

    指标(m)LSPR-Bayes
    平原STD2.78522.0014
    山区STD1.78111.5231
    下载: 导出CSV

    表  4  机载实测数据参数

    雷达参数数值雷达参数数值雷达参数数值
    雷达载频9.6 GHz采样频率125 MHz波长3.13 cm
    脉冲宽度5 μs天线孔径0.4 m飞行高度2600 m
    信号带宽100 MHz脉冲重复频率2000 Hz载机速度60 m/s
    下载: 导出CSV

    表  5  实测数据重跟踪结果定量分析

    指标(m)LSPR-Bayes
    STD14.7412.22
    下载: 导出CSV
  • [1] 刘鹏, 许可, 王磊, 等. 合成孔径雷达高度计与传统高度计精度比对分析与机载试验验证[J]. 电子与信息学报, 2016, 38(10): 2495–2501. doi: 10.11999/JEIT151354

    LIU Peng, XU Ke, WANG Lei, et al. Precision comparison and airborne experiment validation between SAR altimeter and conventional altimeter[J]. Journal of Electronics &Information Technology, 2016, 38(10): 2495–2501. doi: 10.11999/JEIT151354
    [2] QUILFEN Y and CHAPRON B. On denoising satellite altimeter measurements for high-resolution geophysical signal analysis[J]. Advances in Space Research, 2021, 68(2): 875–891. doi: 10.1016/j.asr.2020.01.005
    [3] SHI Lingwei, XU Ke, LIU Peng, et al. Height precision of SAR altimeter and conventional radar altimeter based on flight experimental data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(6): 2676–2686. doi: 10.1109/JSTARS.2016.2550030
    [4] 杨双宝, 翟振和, 许可, 等. 合成孔径雷达高度计数据处理方法[J]. 遥感技术与应用, 2017, 32(6): 1083–1092. doi: 10.11873/j.issn.1004-0323.2017.6.1083

    YANG Shuangbao, ZHAI Zhenhe, XU Ke, et al. The ground process segment of SAR altimeter[J]. Remote Sensing Technology and Application, 2017, 32(6): 1083–1092. doi: 10.11873/j.issn.1004-0323.2017.6.1083
    [5] HALIMI A, MAILHES C, TOURNERET J Y, et al. A semi-analytical model for delay/Doppler altimetry and its estimation algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4248–4258. doi: 10.1109/TGRS.2013.2280595
    [6] NEMETH C and FEARNHEAD P. Stochastic gradient Markov chain Monte Carlo[J]. Journal of the American Statistical Association, 2021, 116(533): 433–450. doi: 10.1080/01621459.2020.1847120
    [7] CHAARI L, TOURNERET J Y, CHAUX C, et al. A Hamiltonian Monte Carlo method for non-smooth energy sampling[J]. IEEE Transactions on Signal Processing, 2016, 64(21): 5585–5594. doi: 10.1109/TSP.2016.2585120
    [8] VAJARGAH K F, BENIS S G, and GOLSHAN H M. Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting[J]. Health Information Science and Systems, 2021, 9(1): 26. doi: 10.1007/s13755-021-00157-5
    [9] BRESSON G, CHATURVEDI A, RAHMAN M A, et al. Seemingly unrelated regression with measurement error: Estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation[J]. The International Journal of Biostatistics, 2021, 17(1): 75–97. doi: 10.1515/ijb-2019-0120
    [10] BAISTHAKUR S and CHAKRABORTY A. Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating[J]. Journal of Civil Structural Health Monitoring, 2021, 11(4): 1093–1112. doi: 10.1007/s13349-021-00495-8
    [11] BROWN G. The average impulse response of a rough surface and its applications[J]. IEEE Transactions on Antennas and Propagation, 1977, 25(1): 67–74. doi: 10.1109/TAP.1977.1141536
    [12] BROWN G S. A useful approximation for the flat surface impulse response[J]. IEEE Transactions on Antennas and Propagation, 1989, 37(6): 764–767. doi: 10.1109/8.29363
    [13] 何华锋, 戴嘉琪, 贺友. 弹载雷达导引头测高回波模型仿真研究[J]. 电光与控制, 2018, 25(11): 11–14,29. doi: 10.3969/j.issn.1671-637X.2018.11.002

    HE Huafeng, DAI Jiaqi, and HE You. Simulation of altimeter echo model for missile-borne radar seeker[J]. Electronics Optics &Control, 2018, 25(11): 11–14,29. doi: 10.3969/j.issn.1671-637X.2018.11.002
    [14] EGIDO A and SMITH W H F. Fully focused SAR altimetry: Theory and applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 392–406. doi: 10.1109/TGRS.2016.2607122
    [15] WARNER B D. Radar terrain return from the spherical earth at near vertical incidence[R]. AD260172, 1961.
    [16] 杨磊, 夏亚波, 毛欣瑶, 等. 基于分层贝叶斯Lasso的稀疏ISAR成像算法[J]. 电子与信息学报, 2021, 43(3): 623–631. doi: 10.11999/JEIT200292

    YANG Lei, XIA Yabo, MAO Xinyao, et al. Sparse ISAR imaging algorithm based on Bayesian-lasso[J]. Journal of Electronics &Information Technology, 2021, 43(3): 623–631. doi: 10.11999/JEIT200292
    [17] YANG Lei, ZHAO Lifan, BI Guoan, et al. SAR ground moving target imaging algorithm based on parametric and dynamic sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2254–2267. doi: 10.1109/TGRS.2015.2498158
    [18] 杨磊, 李埔丞, 李慧娟, 等. 稳健高效通用SAR图像稀疏特征增强算法[J]. 电子与信息学报, 2019, 41(12): 2826–2835. doi: 10.11999/JEIT190173

    YANG Lei, LI Pucheng, LI Huijuan, et al. Robust and efficient sparse-feature enhancement for generalized SAR imagery[J]. Journal of Electronics &Information Technology, 2019, 41(12): 2826–2835. doi: 10.11999/JEIT190173
    [19] PROTIN F, JULES M, NGUYEN D T, et al. Unified modelling of epidemics by coupled dynamics via Monte-Carlo Markov Chain algorithms[J]. arXiv: 2106.13463, 2021.
    [20] YAO Yu and STEPHAN K E. Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models[J]. Human Brain Mapping, 2021, 42(10): 2973–2989. doi: 10.1002/hbm.25431
    [21] HALIMI A, MAILHES C, TOURNERET J Y, et al. Bayesian estimation of smooth altimetric parameters: Application to conventional and delay/Doppler altimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2207–2219. doi: 10.1109/TGRS.2015.2497583
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出版历程
  • 收稿日期:  2022-03-25
  • 修回日期:  2022-05-24
  • 网络出版日期:  2022-06-01
  • 刊出日期:  2023-04-10

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