RESEARCH PAPER
Kalman Filter Realization for Orientation and Position Estimation on Dedicated Processor
1
Department of Automatic Control and Robotics, Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland
Online publication date: 2014-08-15
Acta Mechanica et Automatica 2014;8(2):88-94
KEYWORDS
ABSTRACT
This paper presents Kalman filter design which has been programmed and evaluated in dedicated STM32 platform. The main aim of the work performed was to achieve proper estimation of attitude and position signals which could be further used in unmanned aeri-al vehicle autopilots. Inertial measurement unit and GPS receiver have been used as measurement devices in order to achieve needed raw sensor data. Results of Kalman filter estimation were recorded for signals measurements and compared with raw data. Position actualization frequency was increased from 1 Hz which is characteristic to GPS receivers, to values close to 50 Hz. Furthermore it is shown how Kalman filter deals with GPS accuracy decreases and magnetometer measurement noise.
REFERENCES (31)
1.
Ahn H.-S., Won C.-H. (2009), DGPS/IMU Integration-Based Geolo-cation System: Airborne Experimental Test Results, Aerospace Science and Technology, 13, 316-324.
2.
Ali J., Ullah Baig Mirza M. R. (2010), Performance Comparison among Some Nonlinear Filters for a Low Cost SINS/GPS Integrated Solution, Nonlinear Dynamics, 61, 491-502.
3.
Bar-Shalom Y., Rong Li X., Kirubarajan T. (2001), Estimation with Applications to Tracking and Navigation, John Wiley & Sons.
5.
Caron F., Duflos E., Pomorski D., Vanheegho P. (2006), GPS/IMU Data Fusion using Multisensor Kalman Filtering: Introduction of Contextual Aspects, Information Fusion, 7, 221-230.
6.
Chen T., Xu S. (2010), Double Line-of-sight Measuring Relative Navigation for Spacecraft Autonomous Rendezvous, Acta Astro-nautica, 67, 122-134.
7.
Franca Junior J. A., Morgado J. A. (2010), Real Time Implementation of a Low-Cost INS/GPS System using xPC Target, Journal of Aerospace Engineering, Sciences and Applications, Vol. 2, No. 3.
8.
Gibbs B. P. (2011), Advanced Kalman Filtering, Least-Squares and Modelling, John Wiley & Sons.
9.
Gosiewski Z., Ortyl A. (1999), Algorithms of Inertial Guidance System and the Position of the Object of Spatial Motion (in Polish), Scientific Publishers Division of the Institute of Aviation system.
10.
Grewal M. S., Andrews A. P. (2008), Kalman Filtering: Theory and Practice Using MATLAB, John Wiley & Sons.
11.
Haid M., Breitenbach J. (2004), Low Cost Inertial Orientation Tracking with Kalman Filter, Applied Mathematics and Computation, 153, 567-575.
12.
Han S., Wang J. (2012), Integrated GPS/INS Navigation System with Dual-Rate Kalman Filter, GPS Solutions, 16, 389-404.
13.
Hongwei B., Zhihua J., Tian Wei F. (2006), IAE-adaptive Kalman Filter for INS/GPS Integrated Navigation System, Journal of Systems Engineering and Electronics, Vol. 17, No. 3, 502-508.
14.
Lee C. R., Salcic Z. (1997), High-performance FPGA-Based Implementation of Kalman Filter, Microprocessors and Microsystems, 21, 257-265.
15.
Luo Y., Wu W., He X. (2012), Double-filter Model with Modified Kalman Filter for Baseband Signal Pre-processing with Application to Ultra Tight GPS/INS Integration, GPS Solutions, 16, 463-476.
16.
Mohamed A. H., Schwarz K. P. (1999), Adaptive Kalman Filtering for INS/GPS, Journal of Geodesy, 73, 193-203.
17.
Ning X., Fang J. (2007), An Autonomous Celestial Navigation Method for LEO Satellite Based on Unscented Kalman filter and Information Fusion, Aerospace Science and Technology, 11, 222-228.
18.
Pace S. (1996), The Global Positioning System: Policy Issues for an Information Technology, Space Policy, 12, 265-275.
19.
Romaniuk S. (2013), Autopilot Measurement Systems Research, Master Thesis, Bialystok University of Technology.
20.
Rush J. (2000), Current Issues in the Use of the Global Positioning System Aboard Satellites, Acta Astronautica, 47, 377-387.
21.
Shojaei K., Mohammad Shahri A. (2011), Experimental Study of Iterated Kalman Filters for Simultaneous Localization and Mapping of Autonomous Mobile Robots, Journal of Intelligent and Robotic Systems, 63, 575-594.
22.
Simon D. (2001), Kalman Filtering, Embedded Systems Programming, June 2001, 72-79.
23.
Sun W., Wang D., Xu L., Xu L. (2013), MEMS-Based Rotary Strapdown Inertial Navigation System, Measurement, 46, 2585-2596.
24.
Titterton D. H., Weston J. L. (1997), Strapdown Inertial Navigation Technology, Institution of Electrical Engineers.
25.
Wagner J. F., Kasties G. (2004), Applying the Principle of Integrated Navigation Systems to Estimating the Motion of Large Vehicles, Aerospace Science and Technology, 8, 155-166.
26.
Wendel J., Schlaile C., Trommer G. F. (2001), Direct Kalman Filtering of GPS/INS for Aerospace Applications, International Symposium on Kinematic Systems in Geodesy, Geomatics and Navigation (KIS2001), Canada.
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| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
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