Open AccessBook
Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions
TLDR
The Discrete Kalman Filter (DLF) as mentioned in this paper is a state-space model based on the continuous Kalman filter (CKF) and is used for estimating the probability and random variables of a linear system to random inputs.Abstract:
Probability and Random Variables: A Review. Mathematical Description of Random Signals. Response of Linear Systems to Random Inputs. Wiener Filtering. The Discrete Kalman Filter, State-Space Modeling, and Simulation. Prediction, Applications, and More Basics on Discrete Kalman Filtering. The Continuous Kalman Filter. Smoothing. Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering. More on Modeling: Integration of Noninertial Measurements Into INS. The Global Positioning System: A Case Study. Appendices. Index.read more
Citations
More filters
BookDOI
An Introduction to the Kalman Filter
Greg Welch,Gary Bishop +1 more
TL;DR: The discrete Kalman filter as mentioned in this paper is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error.
Patent
Tracking bimanual movements
TL;DR: In this article, the movement of two occluded hands may be tracked as a unit during an occlusion period, and a type of synchronization characterizing the two hands during the occlusions may be determined based on the tracked movement of the occlated hands.
Journal ArticleDOI
A Survey of Positioning Systems Using Visible LED Lights
Yuan Zhuang,Luchi Hua,Longning Qi,Jun Yang,Pan Cao,Yue Cao,Yongpeng Wu,John Thompson,Harald Haas +8 more
TL;DR: A thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost is undertaken.
Journal ArticleDOI
Autonomous Underwater Vehicle Navigation
TL;DR: This paper considers the vehicle navigation problem for an autonomous underwater vehicle (AUV) with six degrees of freedom using an error state formulation of the Kalman filter, and proposes novel tightly coupled techniques for the incorporation of the LBL and DVL measurements.
Journal ArticleDOI
Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications
TL;DR: A two-tier approach is proposed for improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy modules, which are augmented in the Kalman filtering INS/GPS integration.