Open Access
Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software
Reads0
Chats0
Citations
More filters
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.
Bayesian Filtering and Smoothing
TL;DR: This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework and learns what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
贝叶斯滤波与平滑 (Bayesian filtering and smoothing)
TL;DR: This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework and learns what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
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
The probabilistic data association filter
TL;DR: It has been shown that the optimal state estimator in the presence of data association uncertainty consists of the computation of the conditional pdf of the state x(k) given all information available at time k, namely, the prior information about the initial state, the intervening known inputs, and the sets of measurements through time k.
References
More filters
Book
Linear System Theory
TL;DR: In this article, a mathematical notation and review of state equation representation state equation solution transition matrix properties two important cases internal stability Lyapunov stability criteria additional stability criteria controllability and observability realizability minimal realization input-out-put stability controller and observer forms linear feedback state observation polynomial fraction description polynoial fraction applications geometric theory applications of geometric theory.
Journal ArticleDOI
A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems
Alan S. Willsky,Harold L. Jones +1 more
TL;DR: In this article, the authors consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times and devise an adaptive filtering system for the detection and estimation of the jumps.
Journal ArticleDOI
Detection and diagnosis of sensor and actuator failures using IMM estimator
Youmin Zhang,X. R. Li +1 more
TL;DR: The proposed approach is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes.
Journal ArticleDOI
A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements
Taek Lyul Song,Jason L. Speyer +1 more
TL;DR: In this article, a modified gain extended Kalman observer (MGEKO) was developed for a special class of systems and a sufficient condition for the estimation errors of the MGEKF to be exponentially bounded in the mean square was obtained.
Journal ArticleDOI
Brief paper: Detection and estimation for abruptly changing systems
TL;DR: The problem of state estimation and system structure detection for discrete stochastic dynamical systems with parameters which may switch among a finite set of values is considered and a unified treatment of the existing suboptimal algorithms is provided.