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Distributed estimation over sensor networks based on distributed conjugate gradient strategies

机译：基于分布式共轭梯度策略的传感器网络分布式估计

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摘要

This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modified CG (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least-mean square-based algorithms and a performance that is close to recursive least-squares algorithms. In comparison with existing centralised or distributed estimation strategies, key features of the proposed algorithms are: (i) more accurate estimates and faster convergence speed can be obtained and (ii) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications.
机译：这项研究提出了用于在无线传感器网络上进行分布式参数估计和频谱估计的分布式共轭梯度（CG）算法。特别是，采用增量和扩散自适应协作策略开发了分布式常规CG（CCG）和改进的CG（MCG）算法。与基于最小均方的算法相比，分布式CCG和MCG算法在均方误差方面具有改进的性能，并且其性能接近于递归最小二乘算法。与现有的集中式或分布式估计策略相比，所提出算法的主要特点是：（i）估计更准确，收敛速度更快；（ii）CG算法的预处理器设计，可以提高算法的性能。提出了CG算法。仿真显示了所提出的CG算法相对于先前报道的用于分布式参数估计和分布式频谱估计应用的技术的性能。

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