Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
复制标题
使用再现核的外部场的严格分散自适应估计
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
D. Stilwell
中科院分区：
文献类型：
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作者：
Jia Guo;Michael E. Kepler;S. Paruchuri;Haoran Wang;A. Kurdila;D. Stilwell
本文描述了一种连续时间的自适应方法，用于由N个智能体组成的团队估计外部场。每个代理i探索有界感兴趣子集Ω <$X：= R的子域Ω。理想的自适应估计的每一个代理从一个分布参数系统（DPS），采取的标量值再生核希尔伯特空间HX的功能在X的值。代理i的理想局部估计的演化近似仅使用代理i在精细时间尺度上的观测来构造。由于精细时间尺度上的局部估计是为每个代理独立构造的，我们说该方法是严格分散的。在粗略的时间尺度上，经由使用从属于覆盖{Ω}i=1，.，Ω的N。可实现的算法是通过构建有限维近似的DPS中定义的每个代理从样本沿着他们的轨迹分散基地。在有限维近似的误差的收敛速度推导出的填充距离的样本，定义在每个子域中的分散中心。通过数值模拟说明了分散估计方法的收敛速度的定性性能。指标项一致性估计，再生核
This paper describes an adaptive method in continuous time for the estimation of external fields by a team of N agents. The agents i each explore subdomains Ω of a bounded subset of interest Ω ⊂ X := R. Ideal adaptive estimates ĝ t are derived for each agent from a distributed parameter system (DPS) that takes values in the scalar-valued reproducing kernel Hilbert space HX of functions over X . Approximations of the evolution of the ideal local estimate ĝ t of agent i is constructed solely using observations made by agent i on a fine time scale. Since the local estimates on the fine time scale are constructed independently for each agent, we say that the method is strictly decentralized. On a coarse time scale, the individual local estimates ĝ t are fused via the expression ĝt := ∑N i=1 Ψ ĝ t that uses a partition of unity {Ψ}1≤i≤N subordinate to the cover {Ω}i=1,...,N of Ω. Realizable algorithms are obtained by constructing finite dimensional approximations of the DPS in terms of scattered bases defined by each agent from samples along their trajectories. Rates of convergence of the error in the finite dimensional approximations are derived in terms of the fill distance of the samples that define the scattered centers in each subdomain. The qualitative performance of the convergence rates for the decentralized estimation method is illustrated via numerical simulations. Index Terms consensus estimation, reproducing kernel
影响因子：
3.6
作者：
Gao, Tingran;Kovalsky, Shahar Z.;Daubechies, Ingrid
通讯作者：
Daubechies, Ingrid