Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models

The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters o...

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Main Author: Piotr Kaniewski
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/6/1584
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spelling doaj-5b5b0ffad9d44f1ebadfa6d9edf0ef872020-11-25T03:50:59ZengMDPI AGSensors1424-82202020-03-01206158410.3390/s20061584s20061584Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement ModelsPiotr Kaniewski0Military University of Technology, ul. gen. S. Kaliskiego 2, 00-908 Warszawa, PolandThe paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.https://www.mdpi.com/1424-8220/20/6/1584extended kalman filteradaptive filterlinearizationnonlinear system model
collection DOAJ
language English
format Article
sources DOAJ
author Piotr Kaniewski
spellingShingle Piotr Kaniewski
Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
Sensors
extended kalman filter
adaptive filter
linearization
nonlinear system model
author_facet Piotr Kaniewski
author_sort Piotr Kaniewski
title Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
title_short Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
title_full Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
title_fullStr Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
title_full_unstemmed Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
title_sort extended kalman filter with reduced computational demands for systems with non-linear measurement models
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-03-01
description The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.
topic extended kalman filter
adaptive filter
linearization
nonlinear system model
url https://www.mdpi.com/1424-8220/20/6/1584
work_keys_str_mv AT piotrkaniewski extendedkalmanfilterwithreducedcomputationaldemandsforsystemswithnonlinearmeasurementmodels
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