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This paper will discuss the design and implementation of an inertial navigation system (INS) using an inertial measurement unit (IMU) and GPS. The INS is capable of providing continuous estimates of a vehicle’s position and orientation. Typically IMU’s are very expensive sensors, however this INS will use a “low cost” version costing only $5,000. Unfortunately with low cost also comes low performance and is the main reason for the inclusion of GPS into the system. Thus the IMU will use accelerometers and gyros to interpolate between the 1Hz GPS positions. All important equations regarding navigation are presented along with discussion. Results are presented to show the merit of the work and highlight various aspects of the INS.

Matlab - Kalman filtering Theory and practice using MATLAB 2nd Edition.pdfMatlab - Kalman filtering Theory and practice using MATLAB 2nd Edition.pdfMatlab - Kalman filtering Theory and practice using MATLAB 2nd Edition.pdfMatlab - Kalman filtering Theory and practice using MATLAB 2nd Edition.pdf

Optimal filtering with Kalman filters and smoothers – a Manual for Matlab toolbox EKF/UKF Jouni Hartikainen and Simo Särkkä Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, P.O.Box 9203, FIN-02015 TKK, Espoo, Finland [email protected], [email protected] February 25, 2008 Version 1.2 Abstract In this paper we present a documentation for optimal filtering toolbox for mathematical software package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter and unscented Kalman filter for discrete time state space models. Algorithms for multiple model systems are provided in the form of Interacting Multiple Model (IMM) filter and it’s non-linear extensions, which are based on banks of extended and unscented Kalman filters. Also included in the toolbox are the Rauch-Tung-Striebel and two-filter smoother counter-parts for each filter, which can be used to smooth the previous state estimates, after obtaining new measurements. The usage and function of each method are illustrated with eight demonstrations problems.

EKF UKF Toolbox for Matlab V1.2 In this paper we present a documentation for optimal filtering toolbox for mathematical software package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter and unscented Kalman filter for discrete time state space models. Algorithms for multiple model systems are provided in the form of Interacting Multiple Model (IMM) filter and it’s non-linear extensions, which are based on banks of extended and unscented Kalman filters. Also included in the toolbox are the Rauch-Tung-Striebel and two-filter smoother counter-parts for each filter, which can be used to smooth the previous state estimates, after obtaining new measurements. The usage and function of each method are illustrated with eight demonstrations problems.

配合卡尔曼滤波Matlab工具箱使用 This manual is a user’s guide for the KALMTOOL toolbox; a MATLAB toolbox containing functions for state estimation for nonlinear systems. The toolbox contains the well-known Extended Kalman Filter (EKF) and two new filters called the DD1 filter and the DD2 filter.