Asymptotic Analysis of the Convergence Time of Autoregressive Kalman Filters
| Autor: | Jose A. Lopez-Salcedo, Sergi Locubiche-Serra, Gonzalo Seco-Granados |
|---|---|
| Přispěvatelé: | Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Institución Catalana de Investigación y Estudios Avanzados, Ministerio de Economía y Competitividad (España) |
| Rok vydání: | 2020 |
| Předmět: |
Asymptotic analysis
Autoregressive model Noise measurement Computer science Applied Mathematics Signal Processing Convergence (routing) 0202 electrical engineering electronic engineering information engineering Applied mathematics 020206 networking & telecommunications 02 engineering and technology Kalman filter Electrical and Electronic Engineering |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2020.2993174 |
| Popis: | In recent years, the Kalman filter has become the prime approach for estimating parameters that evolve following some dynamic model and prior statistics. In addition, recent contributions are introducing the use of autoregressive models in the state-space formulation to deal with correlated Gaussian-distributed magnitudes. However, the derivation of closed-form expressions for predicting their performance during the design stage is still an open problem. In that regard, in this letter we derive novel approximate closed-form upper bounds to characterize the convergence time of autoregressive Kalman filters. To this end, we extend a batch mode-based approach previously proposed in the literature that reveals the need for a dedicated dual-asymptotic analysis for this kind of techniques. Simulations are provided to show the goodness of the derived results. This work was supported in part by the Spanish Ministry of Economy and Competitiveness Project under Grant TEC2017-89925-R and in part by the ICREA Academia program. |
| Databáze: | OpenAIRE |
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