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Proceedings ArticleDOI

Behavior of the discrete-time Kalman filter under incorrect noise covariances

Suwanchai Sangsuk-Iam, +1 more
- Vol. 26, Iss: 26, pp 1594-1600
TLDR
In this article, the authors studied the behavior of the discrete-time Kalman filter under incorrect noise covariances and quantified the filter performance by the actual one-step predictor error covariance.
Abstract
In this paper, we study the behavior of the discrete-time Kalman filter under incorrect noise covariances. In particular, we are interested in the characteristic of the actual performance of the Kalman filter. The filter performance is quantified by the actual one-step predictor error covariance. Convergence and divergence analyses of the actual one-step predictor error covariance are given. The results developed in the paper provide useful insights in the behavior of the Kalman filter when the noise covariances used in designing the filter are inexact.

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Citations
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Analysis of discrete-time Kalman filtering under incorrect noise covariances

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Tracking targets using adaptive Kalman filtering

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Widely Linear Modeling for Frequency Estimation in Unbalanced Three-Phase Power Systems

TL;DR: It is shown that the Clarke's transformed three-phase voltage is circular for balanced systems and noncircular for unbalanced ones, making the proposed widely linear estimation perfectly suited both to identify the fault and to provide accurate estimation in unbalanced conditions, critical issues where standard models typically fail.
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Student's $t$ -Filters for Noise Scale Estimation

TL;DR: It is shown that under appropriate conditions, the filter both estimates the state and re-scales the noise covariance matrices in a Kullback–Leibler optimal fashion.
Journal ArticleDOI

Analysis of continuous-time Kalman filtering under incorrect noise covariances

TL;DR: The results presented in the paper help one to understand and be able to predict certain behavior of the Kalman filter when inexact values of noise covariances are used.