Journal ArticleDOI
A Kalman Filter Based Tracking Scheme with Input Estimation
Yiu-Tong Chan,A.G.C. Hu,J. Plant +2 more
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TLDR
In this article, a least square estimator is used to estimate the acceleration input vector of a target and a simple Kalman filter is used for tracking the target in constant course and speed mode.Abstract:
Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.read more
Citations
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Journal ArticleDOI
Survey of maneuvering target tracking. Part I. Dynamic models
X. Rong Li,Vesselin P. Jilkov +1 more
TL;DR: A comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the measurement-origin uncertainty is presented in this article, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion.
Journal ArticleDOI
Design of an interacting multiple model algorithm for air traffic control tracking
X.R. Li,Y. Bar-Shalom +1 more
TL;DR: The design of a tracking algorithm based on the interacting multiple model (IMM) configuration for a generic air traffic control tracking problem is presented and significant noise reduction is achieved during the uniform motion while maintaining the accuracy of the state estimates better than the unfiltered raw radar measurements during the maneuver.
Journal ArticleDOI
Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm
TL;DR: In this paper, the authors compared two maneuvering-target tracking techniques, called input estimation and switching of the target state model, where the various state models can be of different dimension and driven by process noises of different intensities, and estimated the state according to the interacting multiple model (IMM) algorithm.
Journal ArticleDOI
Tracking a Maneuvering Target Using Input Estimation
TL;DR: Chan, Hu, and Plant as discussed by the authors proposed a solution to this problem which used themean deviations of the residual innovation sequence to make corrections to the Kalman filter, for which an Implementable closed-form recursive relation exists.
Journal ArticleDOI
Variable Dimension Filter for Maneuvering Target Tracking
Yaakov Bar-Shalom,K. Birmiwal +1 more
TL;DR: In this article, a novel approach to tracking a maneuvering target is developed, which does not rely on a statistical description of the maneuver as a random process, instead, the state model for the target is changed by introducing extra state components when a maneuver is detected.
References
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Journal ArticleDOI
Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
TL;DR: In this paper, an optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets, using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when the target is to be tracked by sensors providing any combination of range, bearing, and elevation measurements.
Journal ArticleDOI
A tutorial introduction to estimation and filtering
TL;DR: In this paper, the basic principles of least squares estimation are introduced and applied to the solution of some filtering, prediction, and smoothing problems involving stochastic linear dynamic systems.
Journal ArticleDOI
Maneuvering Target Tracking Using Adaptive State Estimation
TL;DR: Two approaches to a nonlinear state estimation problem of tracking a maneuvering target in three-dimensional space using spherical observations (radar data) rely on semi-Markov modeling of target maneuvers and result in effective algorithms that prevent the loss of track when a target makes a sudden, radical change in its trajectory.
Journal ArticleDOI
A Decision - Directed Adaptive Tracker
R.J. McAulay,E. Denlinger +1 more
TL;DR: In this article, a decision-directed adaptive tracker is proposed to detect the aircraft maneuver. But the tracker performs on the basis of a piecewise linear model in which the breakpoints are defined on-line using the maneuver detector.
Book
State space analysis of control systems
TL;DR: In order to introduce some key ideas in state-variable system modeling, the authors need to use signal-flow graphs, which allow for only three types of operations: addition of incoming signals at a node, amplification by a fixed factor, and integration.