Journal ArticleDOI
Tracking a Maneuvering Target Using Input Estimation
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TLDR
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.Abstract:
The conventional Kalman tracking filter incurs mean tracking errors in the presence of a pilot-induced target maneuver. Chan,Hu, and Plant proposed a solution to this problem which used themean deviations of the residual innovation sequence to make corrections to the Kalman filter. This algorithm is further developedhere for the case of a one-dimensional Kalman filter, for which an Implementable closed-form recursive relation exists. Simulation results show that the Chan, Hu, and Plant method can accurately detect and correct an acceleration discontinuity under a variety of maneuver models and radar parameters. Also, the inclusion of thislogic into a multiple hypothesis tracking system is briefly outlined.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
Interacting multiple model methods in target tracking: a survey
TL;DR: The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems, with special attention to the assumptions underlying each algorithm and its applicability to various situations.
Journal ArticleDOI
Survey of maneuvering target tracking. Part V. Multiple-model methods
X. Rong Li,Vesselin P. Jilkov +1 more
TL;DR: A comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty is presented in this article, which is centered around three generations of algorithms: autonomous, cooperating, and variable structure.
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.
References
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Book
Applied Optimal Estimation
TL;DR: This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation, and the theory and practice of optimal estimation is presented.
Journal ArticleDOI
An algorithm for tracking multiple targets
TL;DR: An algorithm for tracking multiple targets in a cluttered environment is developed, capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports.
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
Optimal adaptive estimation of sampled stochastic processes
TL;DR: In this article, an adaptive approach to the problem of estimating a sampled, stochastic process described by an initially unknown parameter vector is presented, which is composed of a set of elemental estimators and a corresponding set of weighting coefficients, one pair for each possible value of the parameter vector.
Journal ArticleDOI
A Kalman Filter Based Tracking Scheme with Input Estimation
Yiu-Tong Chan,A.G.C. Hu,J. Plant +2 more
TL;DR: 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.