论文标题
传感器降解和贝叶斯多传感器数据融合方法的迭代校正
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
论文作者
论文摘要
我们提出了一种新的方法，用于从多个降解信号中推断受不同量传感器暴露的基础真相信号。该算法通过仅从它们之间的比率对两个信号进行两个信号进行迭代校正来学习乘法降解效应。降解函数D应该是连续的，满足单调性，并且D（0）= 1。我们使用平滑的单调回归方法，在此我们很容易将上述标准纳入拟合部分。我们包括理论分析，并证明无噪声测量模型的地面信号会聚。最后，我们提出了一种使用高斯过程融合噪声校正信号的方法。我们使用稀疏的高斯工艺，可以用于大量测量以及可以估算所有传感器噪声值的专门内核。数据融合框架自然处理数据差距，并提供了一种简单而强大的方法，用于观察多个时间尺度（长期和短期信号属性）上的信号趋势。校正方法的生存能力在具有已知地面真实信号的合成数据集上进行评估。
We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should be continuous, satisfy monotonicity, and d(0) = 1. We use smoothed monotonic regression method, where we easily incorporate the aforementioned criteria to the fitting part. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model. Lastly, we present an approach to fuse the noisy corrected signals using Gaussian processes. We use sparse Gaussian processes that can be utilized for a large number of measurements together with a specialized kernel that enables the estimation of noise values of all sensors. The data fusion framework naturally handles data gaps and provides a simple and powerful method for observing the signal trends on multiple timescales(long-term and short-term signal properties). The viability of correction method is evaluated on a synthetic dataset with known ground-truth signal.
