A filtering algorithm for maneuvering target tracking based on smoothing spline fitting
type: Item
instance of: scholarly article
title: A filtering algorithm for maneuvering target tracking based on smoothing spline fitting
zbMATH Open document ID: 1468.93171
DOI: 10.1155/2014/127643
author: Xianqiang Yang
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: Maneuvering target tracking is a challenge. Target's sudden speed or direction changing would make the common filtering tracker divergence. To improve the accuracy of maneuvering target tracking, we propose a tracking algorithm based on spline fitting. Curve fitting, based on historical point trace, reflects the mobility information. The innovation of this paper is assuming that there is no dynamic motion model, and prediction is only based on the curve fitting over the measured data. Monte Carlo simulation results show that, when sea targets are maneuvering, the proposed algorithm has better accuracy than the conventional Kalman filter algorithm and the interactive multiple model filtering algorithm, maintaining simple structure and small amount of storage.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 65D07
zbMATH DE Number: 7021763
Wikidata QID: Q59035313
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/127643
OpenAlex ID: W2053931670
cites work: The interacting multiple model algorithm for systems with Markovian switching coefficients
cites work: Approaches to adaptive filtering
cites work: Asymptotic parameter estimation for a class of linear stochastic systems using Kalman-Bucy filtering
description: scientific article; zbMATH DE number 7021763