On a consistent procedure for distributed recursive nonlinear least-squares estimation

Soummya Kar, Jose M.F. Moura, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper studies recursive nonlinear least squares parameter estimation in inference networks with observations distributed across multiple agents and sensed sequentially over time. Conforming to a given inter-agent communication or interaction topology, distributed recursive estimators of the consensus + innovations type are presented in which at every observation sampling epoch the network agents exchange a single round of messages with their communication neighbors and recursively update their local parameter estimates by simultaneously processing the received neighborhood data and the new information (innovation) embedded in the observation sample. Under rather weak conditions on the connectivity of the inter-agent communication and a global observability criterion, it is shown that the proposed algorithms lead to consistent parameter estimates at each agent. Furthermore, under standard smoothness assumptions on the sensing nonlinearities, the distributed estimators are shown to yield order-optimal convergence rates, i.e., as far as the order of pathwise convergence is concerned, the local agent estimates are as good as the optimal centralized nonlinear least squares estimator having access to the entire network observation data at all times.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages891-894
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

Keywords

  • Collaborative network processing
  • Consensus + innovations
  • Distributed estimation
  • Distributed stochastic aproximation
  • Multi-agent networks
  • Nonlinear least squares

Fingerprint Dive into the research topics of 'On a consistent procedure for distributed recursive nonlinear least-squares estimation'. Together they form a unique fingerprint.

  • Cite this

    Kar, S., Moura, J. M. F., & Poor, H. V. (2013). On a consistent procedure for distributed recursive nonlinear least-squares estimation. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 891-894). [6737035] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6737035