Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48823, USA.
Sensors (Basel). 2011;11(3):3051-66. doi: 10.3390/s110303051. Epub 2011 Mar 9.
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.
本文提出了一类新的自组织传感代理，它们使用噪声测量自适应地学习各向异性、时空高斯过程，并移动以提高估计协方差函数的质量。这种方法基于一类高斯过程的各向异性协方差函数，用于模拟广泛的时空物理现象。协方差函数被假设为先验未知。因此，它是通过最大后验概率 (MAP) 估计器进行估计的。然后根据协方差函数的 MAP 估计值获得感兴趣区域的预测。提出了一种最优采样策略来最小化 Fisher 信息矩阵的信息论代价函数。仿真结果证明了所提出方案的有效性和适应性。
