Journal ArticleDOI
Real-Time Tracking Filter Evaluation and Selection for Tactical Applications
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TLDR
In this article, five important tracking filters that are often candidates for implementation in systems that must track maneuvering vehicles are compared in terms of tracking accuracy and computer requirements for tactical applications.Abstract:
Five important tracking filters that are often candidates for implementation in systems that must track maneuvering vehicles are compared in terms of tracking accuracy and computer requirements for tactical applications. A rationale for selecting among these filters, which include a Kalman filter, a simplified Kalman filter, an ?-s filter, a Wiener filter, and a two-point extrapolator, is illustrated by two examples taken from the authors' recent experience.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
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.
Book
Tracking and Kalman Filtering Made Easy
TL;DR: In this paper, the authors present a general form for linear time-invariant systems, including least-squares and minimum-variance estimates for Linear Time-Invariant Systems.
Journal ArticleDOI
Digital signal processing for sonar
TL;DR: This paper is a tutorial which describes "main stream" sonar digital signal processing functions along with the associated implementation considerations to promote further cross-fertilization of ideas amongdigital signal processing applications in sonar, radar, speech, communications, seismology, and other related fields.
Book
Visual Control of Robots: High-Performance Visual Servoing
TL;DR: This book is about the application of high-speed machine vision for close-loop position control, or visual servoing, of a robot manipulator and provides a comprehensive coverage of all aspects of the visual Servoing problem: robotics, vision, control, technology and implementation issues.
References
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Journal ArticleDOI
On the identification of variances and adaptive Kalman filtering
TL;DR: In this paper, it was shown that the steady-state optimal Kalman filter gain depends only on n \times r linear functionals of the covariance matrix and the number of unknown elements in the matrix.
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
Synthesis of an optimal set of radar track-while-scan smoothing equations
T. Benedict,G. Bordner +1 more
TL;DR: In this article, a set of position-and velocity tracking equations is synthesized by a calculus-of-variations technique, and the resulting optimally synthesized set characterizes the commonly termed "alpha-\beta " tracker, with the important proviso that \beta=\alpha^{2}/(2 - \alpha).