Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
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DOI：
10.1109/tsp.2007.906734
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发表时间：
2008
影响因子：
5.4
通讯作者：
I. Schizas;Alejandro Ribeiro;G. Giannakis
中科院分区：
文献类型：
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作者：
I. Schizas;Alejandro Ribeiro;G. Giannakis
我们处理分布式估计的确定性矢量参数使用ad hoc无线传感器网络（WSNs）。我们将分散估计问题转化为多个约束凸优化子问题的解。使用乘法器的方法结合块坐标下降的方法，我们演示了如何得到的算法可以分解成一组更简单的任务，适合分布式实现。与现有的替代方案不同，我们的方法不需要集中估计是可表示的，在一个可分离的封闭形式的平均值，从而允许分散计算，甚至非线性估计，包括最大似然估计（MLE）在非线性和非高斯数据模型。我们证明了这些算法有保证收敛到所需的估计时，传感器链路假设理想的。此外，我们的分散式算法在存在接收器和/或量化噪声的情况下表现出弹性。特别是，我们引入了一个分散的计划，最小二乘和最佳线性无偏估计（BLUE），并建立其收敛存在的通信噪声。我们的算法也表现出更高的收敛速度与现有计划的潜力。通过仿真验证了新的分布式估计算法的优点。
We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and/or quantization noise. In particular, we introduce a decentralized scheme for least-squares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms.