Convergence analysis of a family of robust Kalman filters based on the\n contraction principle

2017/05/15 by Mattia Zorzi, Zorzi, Mattia · 2 citations
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Mathematics #Optimization and Control (math.OC) #Optimization and Variational Analysis #Stability and Control of Uncertain Systems #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1705.05286

openalex publication_date 2017/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper we analyze the convergence of a family of robust Kalman\nfilters. For each filter of this family the model uncertainty is tuned\naccording to the so called tolerance parameter. Assuming that the corresponding\nstate-space model is reachable and observable, we show that the corresponding\nRiccati-like mapping is strictly contractive provided that the tolerance is\nsufficiently small, accordingly the filter converges.\n

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