NDLI: Linear fitting Kalman filter

Please wait, while we are loading the content...

Doubly iterative multiple-input–multiple-output-bit-interleaved coded modulation receiver with joint channel estimation and randomised sampling detection

Efficient method for finding globally optimal solution of problem with weighted Lp norm and L 2 norm objective function

Entropic uncertainty inequalities on sparse representation

Imaging of high-speed manoeuvering target via improved version of product high-order ambiguity function

Iterative channel estimation for multiuser fibre-wireless uplink exploiting semi-correlated ternary signals

Linear fitting Kalman filter

Model-based super-resolution time-delay estimation with sample rate consideration

Robust anti-jamming method for high dynamic global positioning system receiver

Unbiased, optimal, and in-betweens: the trade-off in discrete finite impulse response filtering

Volume 10 (2016) : Issue 3, May

Volume 10 (2016) : Issue 2, Apr

Volume 10 (2016) : Issue 1, Feb

IET Wireless Sensor Systems

Micro & Nano Letters

The Journal of Engineering

Linear fitting Kalman filter

Content Provider	IET Digital Library
Author	Xiong, Yuanbo Zhong, Xionghu
Abstract	Dynamic estimation in signal processing and target tracking often involves non-linear models. These non-linear models are usually linearised through the first-order Taylor approximation in estimation process. However, the error generated by the first-order Taylor approximation is not negligible when the non-linearity of a model is high or the input error is large. This study proposes a new linearisation method through minimising the error between a non-linear function and its linear approximation. A weighted least squares (WLS) algorithm is developed to estimate a linear fitting (LF) function based on the sigma points of the random variable in non-linear transformation. A linear fitting Kalman filter (LKF) is developed based on this principle. The accuracy of the LF transform is analysed using the Kullback–Leibler (KL) distance. The results show that the LF transform has less KL distance to the true distribution compared with the first-order Taylor approximation. To evaluate the estimation performance, simulations are conducted and the results are compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results demonstrate that the LKF provides better accuracy than the EKF, and has similar accuracy to the UKF with lower computational cost.
Starting Page	404
Ending Page	412
Page Count	9
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 4, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2015.0270
Journal	IET Signal Processing
Publisher Date	2016-06-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computational Complexity Dynamic Estimation EKF Error Minimization Extended Kalman Filter Filtering Method in Signal Processing First-order Taylor Approximation Interpolation And Function Approximation Kalman Filter KL Distance Kullback-Leibler Distance Least Squares Approximation Linear Approximation Linear Fitting Function Estimation Linear Fitting Kalman Filter Linear Fitting Transform Linearisation Method LKF Nonlinear Filter Nonlinear Function Nonlinear Model Nonlinear Transformation Numerical Analysis Random Variable Sigma Point Signal Processing Signal Processing Theory Target Tracking UKF Unscented Kalman Filter Unscented Transform Weighted Least Square Algorithm WLS Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Alternative framework for the iterated unscented Kalman filter

Unscented Kalman filter-aided Gaussian sum filter

Some Relations Between Extended and Unscented Kalman Filters

Approximate Conditional Least Squares Estimation of a Nonlinear State-Space Model via Unscented Kalman Filter (2011)

A Fusion Based Particle Filter TBD Algorithm for Dim Targets

Comparison of estimation accuracy of EKF, UKF and PF filters

State Estimation for Target Tracking Problems with Nonlinear Kalman Filter Algorithms

Instantaneous fundamental frequency estimation of non-stationary periodic signals using non-linear recursive filters

An Application of the Kalman Filter Recursive Algorithm to Estimate the Gaussian Errors by Minimizing the Symmetric Loss Function

Linear fitting Kalman filter

Similar Documents

Alternative framework for the iterated unscented Kalman filter

Unscented Kalman filter-aided Gaussian sum filter

Some Relations Between Extended and Unscented Kalman Filters

Approximate Conditional Least Squares Estimation of a Nonlinear State-Space Model via Unscented Kalman Filter (2011)

A Fusion Based Particle Filter TBD Algorithm for Dim Targets

Comparison of estimation accuracy of EKF, UKF and PF filters

State Estimation for Target Tracking Problems with Nonlinear Kalman Filter Algorithms

Instantaneous fundamental frequency estimation of non-stationary periodic signals using non-linear recursive filters

An Application of the Kalman Filter Recursive Algorithm to Estimate the Gaussian Errors by Minimizing the Symmetric Loss Function

Linear fitting Kalman filter