Position estimation in kalman filter
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Mohamed Freeshah and his classmates did a Final Project for Generalized Adjustment Course, LIESMARS, Wuhan University, 2018
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Position estimation in kalman filter
- 1. 1 Position Estimation in Kalman Filter Presented By David Sestak, Zhang zhen bing, Ahmed Mohamed Reda & Mohamed Freeshah January, 2018
- 2. 2 Position Estimation in Kalman Filter Zhang zhen bing Presented By Ahmed Mohamed Reda Mohamed Ahmed FreeshahDavid Sestak
- 3. 3 ▪ Introduction ▪ Motivation ▪ Equipment ▪ Methodology ➢ Data source ➢ KF Processing ▪ Results Project Schedule
- 4. 4 ▪ Precise position estimation is one of the fundamental technical dependencies in our society today. It has influenced our lives in countless ways most people do not think about. ▪ It is used in a variety of applications including: ▪ GNSS (and all its varying applications) ▪ Self driving vehicles ▪ Mobile robots ▪ Internet of Things (IoT) and many others Introduction
- 5. 5 ▪ Indoor positioning has become an area of increasing interest utilizing the various signals that our devices use such as Bluetooth and WiFi. ▪ People spend about 80% of their time indoors, so it is of great significant to achieve precise indoor position estimation. ▪ This is why we are using a smartphone, a device everyone carries, for real time position estimation only using its inertial sensors. Motivation
- 6. 6 ▪ Smartphone is a versatile device with many sensors such as: MEMS, Camera, WiFi, Bluetooth, GPS, etc. , which provides a high potential for indoor position estimation for many users . ▪ Data source ▪ The HUAWEI P10 smartphone was utilized to acquire the original data, including: the value of the accelerometer, gyroscope, magnetometer ▪ From the accelerometer , we can get the value of acceleration along the X axis and Y axis. ▪ From the magnetometer and gyroscope , we can get the value of angle and angle rate along the way. Equipment
- 7. 7 HUAWEI P10 Sensors: ▪ Fingerprint Sensor, G-Sensor, Gyroscope Sensor, Compass, Ambient Light Sensor, Proximity Sensor, Hall Sensor Software: ▪ HIPE2.3 ▪ MATLAB Equipment
- 8. 8 ▪ The Kalman Filter (KF) is a time-varying linear optimal estimation algorithm , which is ideally suited for position estimation in modern multi-sensor systems. ▪ An important aspect of KF is its recursive nature. The position estimation is only based on the previously calculated state and current input. ▪ This makes it ideal for real time application ▪ KF puts more weight on higher certainty values improving its estimation precision. Why KF ?
- 9. 9 ▪ Our Observation Variables: ▪ ax = x-direction acceleration ▪ ay = y-direction acceleration ▪ θ = direction ▪ ω = angular rate ▪ These are the observation values that our smartphone inertial sensors provides. Methodology
- 10. 10 ▪ Kalman Filter Variables: ▪ A = system function matrix ▪ H = observation function matrix ▪ Q = system noise matrix for compensation ▪ R = observation noise matrix ▪ xk = system state variables ▪ zk = observation variables ▪ Pk = covariance matrix ▪ Note: ▪ - = predicted value ▪ ^ = estimated value Methodology
- 12. 12 Methodology 0 1 2 3 4 5 6 类别 1 类别 2 类别 3 类别 4
- 13. 13 ▪ KF Processing In our project: Assumption:(Gaussian distribution) First: define the state variables Second: confirm the system function according to the physic principle. Third: given the observation equation. Fourth: confirm the matrix of Q, R, and P according to the experience. Methodology
- 14. 14 Methodology K+1 K 0 0 t 0 0 0 0 0 0 0 0 t 0 0 0 0 0 0 1 0 t 0 0 0 0 0 0 1 0 t 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 t 0 0 0 0 0 0 0 1 θ ω θ ω = System function
- 15. 15 Methodology K+1 K+1 θ ω 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 θ ω = Observation function
- 16. 16 Methodology K+1 K+1 θ ω 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 θ ω = Observation function
- 17. 17 Methodology 0.3 0 0 0 0 0 0 0 0 0.3 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 0 16 Q = Covariance matrix of the system noise
- 18. 18 Methodology 0.1 0 0 0 0 0.1 0 0 0 0 36 0 0 0 0 36 R = Covariance matrix of the measurement noise
- 19. 19 Methodology 0.01 0 0 0 0 0 0 0.01 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0.1 0 0 0 0 0 0 36 0 0 0 0 0 0 36 R = Covariance matrix of the measurement noise
- 20. 20 Methodology 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 36 0 0 0 0 0 0 0 0 36 P = Error covariance matrix of the estimation of the state variables
- 21. 21 Observations acceleration (x) Estimated value Observed value
- 25. 25 Observations Velocity (x , y)
- 26. 26 Results Distances (x , y)
- 27. 27 Results Sys. Variables Variance
- 28. 28 Results Final Track Part of play ground in front of LIESMARS (Straight line = 100 m, Curve = 50 m)
- 29. 29 ▪ Errors: ▪ Unreasonable velocity increase ▪ Improvement used: ▪ Counteracting unreasonable velocity increase ▪ Ways to Improve: ▪ Ground Truth ▪ Inertial sensor position update Discussion
- 30. 30 Conclusion Availability Continuity Accuracy Integrity (Ways to Improve by four criteria)
- 31. 31 ▪ Leick, A. L. Rapoport, D. Tatarnikov., 2016, GPS Satellite Surveying, 4th Edition, New York: John Wiley and Sons ▪ G. Welch, G. Bishop., 2001, An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC 27599-3175 ( http://www.cs.unc.edu ) ▪ Kalman, R.E. Trans ASME. 1960; 82:35–45. Crossref | Scopus (12465) ▪ http://consumer.huawei.com/en/phones/p10/specs/ Major References:
- 32. 32 Thank You !