移动无线传感器网络中的顺序信道状态跟踪和SpatioTemporal信道预测
Sequential Channel State Tracking & SpatioTemporal Channel Prediction in Mobile Wireless Sensor Networks
摘要：
我们提出了一个非线性滤波框架接近的问题，在移动无线传感器网络中的信道状态跟踪和时空信道增益预测，在贝叶斯设置。我们假设无线信道构成一个可观察的（由传感器/网络节点），时空，条件高斯随机过程，这是统计依赖于一组隐藏的信道参数，称为信道状态。信道状态根据已知的、非平稳的、非线性的和/或非高斯马尔可夫随机核随时间演变。该公式导致部分可观测系统，具有随时间变化的全局状态和时空变化的观测。认识到一般的非线性状态估计的棘手性，我们提倡使用基于网格的近似滤波器作为一种有效的和鲁棒的手段递归跟踪的信道状态。我们还提出了一个连续的时空预测跟踪信道增益在时间和空间的任何点，提供实时连续估计各自的信道增益图，为网络中的每个传感器。此外，我们表明，这两个估计收敛到真正的各自的MMSE最优估计，在一个共同的，相对较强的意义。数值仿真验证了该方法的有效性。
英文摘要：
We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically dependent on a set of hidden channel parameters, called the channel state. The channel state evolves in time according to a known, non stationary, nonlinear and/or non Gaussian Markov stochastic kernel. This formulation results in a partially observable system, with a temporally varying global state and spatiotemporally varying observations. Recognizing the intractability of general nonlinear state estimation, we advocate the use of grid based approximate filters as an effective and robust means for recursive tracking of the channel state. We also propose a sequential spatiotemporal predictor for tracking the channel gains at any point in time and space, providing real time sequential estimates for the respective channel gain map, for each sensor in the network. Additionally, we show that both estimators converge towards the true respective MMSE optimal estimators, in a common, relatively strong sense. Numerical simulations corroborate the practical effectiveness of the proposed approach.
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