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Distributed incremental bias-compensated RLS estimation over multi-agent networks

基于多代理网络的增量式偏差补偿递归最小二乘算法

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Abstract

In this paper, we study the problem of distributed bias-compensated recursive least-squares (BC-RLS) estimation over multi-agent networks, where the agents collaborate to estimate a common parameter of interest. We consider the situation where both input and output of each agent are corrupted by unknown additive noise. Under this condition, traditional recursive least-squares (RLS) estimator is biased, and the bias is induced by the input noise variance. When the input noise variance is available, the effect of the noise-induced bias can be removed at the expense of an increase in estimation variance. Fortunately, it has been illustrated that distributed collaboration between agents can effectively reduce the variance and can improve the stability of the estimator. Therefore, a distributed incremental BC-RLS algorithm and its simplified version are proposed in this paper. The proposed algorithms can collaboratively obtain the estimates of the unknown input noise variance and remove the effect of the noise-induced bias. Then consistent estimation of the unknown parameter can be achieved in an incremental fashion. Simulation results show that the incremental BC-RLS solutions outperform existing solutions in some enlightening ways.

摘要

创新点:本文研究了基于多代理网络的增量式偏差补偿递归最小二乘算法,并推导了该算法的标准形式及简化形式。当网络中各代理节点受未知输入输出噪声干扰时,传统的递归最小二乘算法的估计结果是有偏的。基于增量式协作策略,本文以分布式信息处理的形式实现对各代理节点的未知输入噪声方差的实时估计,并获得未知参数的无偏估计值。同时,本文研究分析了算法中各变量在各代理间的传递方式对多代理网络性能的影响。仿真结果表明,本文提出的算法估计精度优于非协作式的单代理参数估计精度。与集中式及其他分布式算法相比,本文提出的算法具有一定的优越性。

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61421001).

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Jian Lou, Lijuan Jia, Ran Tao & Yue Wang

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  1. Jian Lou
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  2. Lijuan Jia
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  3. Ran Tao
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  4. Yue Wang
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Correspondence to Lijuan Jia.

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Conflict of interest The authors declare that they have no conflict of interest.

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Lou, J., Jia, L., Tao, R. et al. Distributed incremental bias-compensated RLS estimation over multi-agent networks. Sci. China Inf. Sci. 60, 032204 (2017). https://doi.org/10.1007/s11432-016-0284-2

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