Journal ArticleDOI
MEMS IMU and two-antenna GPS integration navigation system using interval adaptive Kalman filter
Xiufeng He,Yang Le,Wendong Xiao +2 more
TLDR
In this article, an interval filtering algorithm is applied to the uncertain integrated system, where the system parameters uncertainties are described by intervals and the IAKF algorithm has the same structure as the standard extended Kalman filtering algorithm.Citations
More filters
Journal ArticleDOI
Data fusion of radar and image measurements for multi-object tracking via Kalman filtering
Du Yong Kim,Moongu Jeon +1 more
TL;DR: This paper presents a multiple-object tracking system whose design is based on multiple Kalman filters dealing with observations from two different kinds of physical sensors and demonstrates that the implemented system with fused observations considerably enhances tracking performance over a single sensor system.
Journal ArticleDOI
Markerless Human–Manipulator Interface Using Leap Motion With Interval Kalman Filter and Improved Particle Filter
Guanglong Du,Ping Zhang,Xin Liu +2 more
TL;DR: The results show that the system is indeed of high availability and fault tolerance in teleoperation, which means even a novice can easily and successfully control robots with this human-manipulator interface.
Journal ArticleDOI
Online Robot Teaching With Natural Human–Robot Interaction
TL;DR: An online teaching method with the fusion of speech and gesture and a novel method of audio-visual fusion based on text is proposed, which can extract the most useful information from the speech and gestures by transforming them into text.
Journal ArticleDOI
A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering
TL;DR: This paper presents a novel robust single global position system (GPS) navigation algorithm based on dead reckoning and strong tracking filter (STF), which still has strong tracking ability even when precise knowledge of the system models is not available.
Journal ArticleDOI
Magnetic-Based Indoor Localization Using Smartphone via a Fusion Algorithm
TL;DR: A fusion algorithm combining the extended Kalman filter (EKF) and the PF scheme is proposed to address blindness and particle degradation problems in the detection of indoor location based on magnetic fields collected by embedded sensors in smartphones.
References
More filters
Journal ArticleDOI
Filtering and smoothing in an H/sup infinity / setting
TL;DR: In this paper, the problems of filtering and smoothing are considered for linear systems in an H/sup infinity / setting, i.e. the plant and measurement noises have bounded energies (are in L/sub 2/), but are otherwise arbitrary.
Journal ArticleDOI
H∞ estimation for discrete-time linear uncertain systems
TL;DR: In this paper, a Riccati equation approach is proposed to solve the estimation problem and it is shown that the solution is related to two algebraic Riemannian equations, and the estimation error dynamics is quadratically stable and the induced operator norm is kept within a prescribed bound for all admissible uncertainties.
Proceedings ArticleDOI
Filtering and smoothing in an H/sup infinity / setting
TL;DR: In this paper, the problem of filtering and smoothing for linear systems in an H/sup infinity / setting is considered, where the initial condition is assumed to be known, and the noise is in some weighted ball of R/sup n/L/sub 2.
Journal ArticleDOI
Interval Kalman filtering
TL;DR: In this article, the classical Kalman filtering technique is extended to interval linear systems with the same statistical assumptions on noise, for which the classical technique is no longer applicable, and the interval Kalman filter (IKF) is derived, which has the same structure as the classical algorithm, using no additional analysis or computation from such as H/sup /spl infin//-mathematics.
Journal ArticleDOI
Analysis of discrete-time Kalman filtering under incorrect noise covariances
S. Sangsuk-Iam,T. E. Bullock +1 more
TL;DR: In this article, it is shown that under certain stability conditions on the system model, the one-step prediction error covariance matrix will converge to a steady-state solution even when the filter gain is not optimal.