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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >On the Use of Gaussian Random Processes for Probabilistic Interpolation of CubeSat Data in the Presence of Geolocation Error
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On the Use of Gaussian Random Processes for Probabilistic Interpolation of CubeSat Data in the Presence of Geolocation Error

机译：存在地理位置误差的高斯随机过程在CubeSat数据概率插值中的应用

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摘要

With their greatly reduced sizes, low development cost, and rapid construction time, CubeSats have merged as a platform of considerable interest for a wide range of applications, including remote sensing. Many applications require the interpolation of sensor data into a regularly spaced grid for the development of downstream scientific products. This problem is complicated for CubeSat platforms due to potentially significant uncertainties associated with the spatial position of the satellite. In this paper, we present a probabilistic approach to the data interpolation problem in which we estimate both the platform location and data samples on a regular grid given observations corrupted by noise and location error. Our approach is based on a Gaussian process model to connect the measured data to the values on the grid. Two statistical models for positional uncertainties are considered, one based on an assumption of independent errors and another motivated by positional errors associated with a specific platform of interest, the MicroMAS radiometer. In each case, the maximum a posteriori estimate of the positions and the data is generated using an optimized Gaussian process regression (OGPR) method resulting in two algorithms: OGPR-IID and OGPR-PCA. The performance of this approach is tested on both simulated data and advanced technology microwave sounder data where significant improvements both qualitatively and quantitatively relative to traditional interpolation methods are observed.
机译：由于CubeSats的尺寸大大减小，开发成本低，建造时间短，因此已经合并成为一个广泛感兴趣的平台，包括遥感在内的各种应用。许多应用需要将传感器数据插值到规则间隔的网格中，以开发下游科学产品。对于CubeSat平台，由于与卫星的空间位置相关的潜在重大不确定性，此问题变得很复杂。在本文中，我们提出了一种数据插值问题的概率方法，该方法在给定观测值被噪声和位置误差破坏的情况下，在规则网格上估计平台位置和数据样本。我们的方法基于高斯过程模型，将测量数据连接到网格上的值。考虑了两种针对位置不确定性的统计模型，一种基于独立误差的假设，另一种则是由与特定感兴趣的平台MicroMAS辐射计相关的位置误差引起的。在每种情况下，均使用优化的高斯过程回归（OGPR）方法生成位置和数据的最大后验估计，从而产生两种算法：OGPR-IID和OGPR-PCA。在模拟数据和先进技术的微波测深仪数据上均测试了该方法的性能，相对于传统的插值方法，该方法在质和量上均获得了显着改善。

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