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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry
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Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

机译：光滑测高参数的贝叶斯估计：在常规测高和延迟/多普勒测高中的应用

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摘要

This paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms.
机译：本文提出了一种新的贝叶斯策略，用于高度估计高度参量。假定根据独立和不同的高斯分布分布的热噪声和斑点噪声破坏了高程信号。我们介绍了一种先验的方法，可以强制对高度参数进行平稳的时间演化，从而改善其物理解释。使用梯度下降算法优化所得模型的后验分布，该算法允许我们计算未知模型参数的最大估计量。该算法的计算成本低，适合实时应用。使用与常规和延迟/多普勒测高相关联的合成数据和实际数据对所提出的贝叶斯策略和相应的估计算法进行评估。与最新的估算算法相比，对实际Jason-2和CryoSat-2波形的分析显示出参数估算的改进。

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