基于高斯SPAWN的分布式局部线性参数估计
Distributed Local Linear Parameter Estimation using Gaussian SPAWN
AI总结：
本文提出基于高斯Sum-Product算法的分布式局部线性参数估计方法，通过固定大小广播消息实现高效网络参数估计，具有更低的通信成本和更快的收敛速度。
摘要：
我们考虑了估计局部传感器参数的问题，其中局部参数和传感器观测值通过线性随机模型相关联。传感器交换消息并合作以迭代估计自己的局部参数。我们研究了基于信念传播的高斯Sum-Product算法在无线网络中的应用（gSPAWN），该算法在每个传感器处使用固定大小的广播消息。与流行扩散策略相比，gSPAWN算法在每个传感器处的通信成本不随网络密度增加而增加，使其更适合无线传感器网络应用。我们证明了在某些技术充分条件下，gSPAWN算法在均值下收敛且具有均方稳定性，并描述了该算法在非视距环境中的网络定位应用。数值结果表明，gSPAWN在一般情况下比扩散方法收敛更快，通信成本更低，且具有可比的均方误差。
英文摘要：
We consider the problem of estimating local sensor parameters, where the local parameters and sensor observations are related through linear stochastic models. Sensors exchange messages and cooperate with each other to estimate their own local parameters iteratively. We study the Gaussian Sum-Product Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief propagation, but uses fixed size broadcast messages at each sensor instead. Compared with the popular diffusion strategies for performing network parameter estimation, whose communication cost at each sensor increases with increasing network density, the gSPAWN algorithm allows sensors to broadcast a message whose size does not depend on the network size or density, making it more suitable for applications in wireless sensor networks. We show that the gSPAWN algorithm converges in mean and has mean-square stability under some technical sufficient conditions, and we describe an application of the gSPAWN algorithm to a network localization problem in non-line-of-sight environments. Numerical results suggest that gSPAWN converges much faster in general than the diffusion method, and has lower communication costs, with comparable root mean square errors.
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