بررسی تأثیر کاهش نویز مشاهدات مغناطیس سنج، ژیروسکوپ و شتاب سنج در بهبود تعیین موقعیت وسیله نقلیه با الگوریتم فیلتر کالمن

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران

2 استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران

10.22131/sepehr.2021.244449

چکیده

دقت تعیین موقعیت به کیفیت تکنولوژی مورد استفاده بستگی دارد. در حالیکه استفاده از تکنولوژی­های ارائهدهنده کیفیت بالای تعیین موقعیت مستلزم صرف هزینه زیاد و داشتن تخصص بالا جهت استفاده میباشد، عمدتاً تکنولوژی با کیفیت و قیمت پایین تعیین موقعیت ماهواره­ای (GPS) و حسگرهای وضعیتی استفاده می­شوند که در قالب گوشی­های هوشمند بهصورت فراگیر در دسترس میباشند. یکی از نکات متمایز این تکنولوژی­های ارزانقیمت، میزان تأثیرپذیری آنها از عوامل تولیدکننده نویز میباشد. در این مقاله تأثیر بهبود میزان نویز حاصل از حسگرهای تعیین موقعیت و وضعیت گوشی­های همراه بر دقت تعیین موقعیت اشیاء متحرک مانند خودروها با استفاده از تکنیک محلی­سازی[1]  و تلفیق دادههای حاصل از حسگرهای مغناطیسسنج، ژیروسکوپ که در گوشی­های همراه هوشمند وجود دارند بررسی شده است. از حسگرهای مزبور پارامترهای آزیموت و زاویه چرخش (رول) استخراج شده است و این پارامترها بههمراه شتاب­خطی و مختصات جغرافیایی حاصل از GPS برای بهبود موقعیت وسیلهنقلیه در الگوریتم کالمن که یک فیلتر پایینگذر برای نویزهای با فرکانس پایین است، تلفیق شده­اند. نتایج بهدست آمده از اجرای روش پیشنهادی در خط 2 بزرگراه آزادگان شرق به غرب تهران و مقایسه آن با دادههای مرجع نشان داده است که خطای تعیین موقعیت خودرو با گیرنده GPS گوشی هوشمند از 0.8274 متر به 0.6768 متر بدون کاهش نویز در فیلتر کالمن توسعهیافته[2]، کاهش یافته است. با کاهش تدریجی نویز، میزان دقت نتایج حاصل از فیلتر کالمن بین مقادیر 0.6763 تا 0.6771 متر در نوسان بوده است که بیشترین بهبود دقت موقعیت خودرو در اثر کاهش 2 درصدی نویز، به مقدار 0.6763 متر حاصل شده ­است. براساس این نتایج، با وجود اینکه کاهش اثر نویز میتواند منجر به بهبود موقعیت وسیلهنقلیه با استفاده از فیلتر کالمن و مشاهدات حسگرهای گوشی هوشمند شود، نامنظم بودن تغییرات دقت ناشی از کاهش نویز، لزوم یافتن درصد نویز کاهش بهینه را ایجاب می­کند.



[1]- Dead-reckoning
 


[2]- Extended  Kalman Filter
 

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the effects of noise reduction in observations made by magnetometer, gyroscope and accelerometer on vehicle positioning with Kalman Filter Algorithm

نویسندگان [English]

  • Kobra Bozorgniya 1
  • Hani Rezayan 2
  • Javad Sadidi 2
1 M.Sc student of remote sensing & geographical information system,faculty of geographical sciences, Kharazmi University, Tehran,Iran
2 Assistant professor of department geoinformatics, faculty of geographical sciences,KharazmiUniversity,Tehran,Iran
چکیده [English]

Introduction
The accuracy of positioning depends on the quality of the technology used. Various technologies and techniques are used for positioning which are classified as absolute and dead-reckoning groups. Classified as absolute positioning technologies,GPS receiversface a variety of different errors in the real-time positioning of a moving object, which reduces the accuracy and precision of the position received from these receivers. On the other hand, dead-reckoning sensors such as gyroscopes and magnetometers which measure real-time state of a moving object also have cumulative errors.Therefore, observations made by all of these sensors are not free from the noise generated during the measurement process.The amount of this noise may vary depending on various factors, including the precision of the sensor and features of the measuring environment. Thus,due to thecorrelation between observations made by these two categories of sensors and the difference between their precision and the nature of their errors,ifnoise is reduced inobservations made by them, their complementary features can be used to reduce errors made by each of them.High-quality positioning technologies are expensive and require high expertise.As a result,lower quality and cheaper global navigation satellite systems (like GPS) widelyavailable in smartphones are more commonly used. One of the most important features of these inexpensive technologies is that they are highly susceptible to factors producing noise.
 
Methodology
The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by  with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of   for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE.
 
Results and Discussions
The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.

کلیدواژه‌ها [English]

  • Vehicle Positioning
  • Global Positioning System
  • Magnetometer
  • Gyroscope
  • Local Positioning
  • Kalman filter
1- Andersson, D,Fjellstrom, J(2004). Vehicle Positioning with Map Matching Using Integration of a Dead Reckoning System and GPS,Andersson,H,Enqvist,M,Linköpings University, Department of Systems Engineering
2- Barrios, C., & Motai, Y. (2011). Improving Estimation of Vehicles Trajectory Using the Latest Global Positioning System With Kalman Filtering. IEEE Transactions on Instrumentation and Measurement,60(12), 3747-3755. doi:10.1109/tim.2011.2147670
3- Barrios, C., Himberg, H., Motai, Y., & Sad, A. (2006). Multiple model framework of adaptive extended kalman filtering for predicting vehicle location. 2006 IEEE Intelligent Transportation Systems Conference. doi:10.1109/itsc.2006.1707361
4- Berdjag,Pomorski, D,D(2004). DGPS-INS data fusion for land navigation. The 7th IEEE International Conference on Information Fusion (FUSION’2004),Stockholm, Sweden. ⟨hal-01509811⟩
5- Bohg,J,(2005). Real-Time Structure from Motion Using Kalman Filtering, Pitzsch,T, Techishe University Dvesden
6- Boukerche, A., Oliveira, H. A., Nakamura, E. F., & Loureiro, A. A. (2008). Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems. Computer Communications,31(12), 2838-2849. doi:10.1016/j.comcom.2007.12.004
7- Chui, C. K., & Chen, G. (1999). Kalman filtering: With real time applications. Berlin: Springer.
8- Diniz, P. S. (2013). Adaptive Filtering Algorithms and Practical Implementation. Boston, MA: Springer US. doi:https://doi.org/10.1007/978-0-387-68606-6
9- Du, Gerdtman, & Lindén, J.,C., M. (2018). Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review. Sensors,18(4), 1123. doi:10.3390/s18041123
10- Ghaleb, F., Zainal, A., Rassam, M., & Abraham, A. (2017). Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter. Pervasive and Mobile Computing, 40, 139-155. doi:10.1016/j.pmcj.2017.06.008
11- Grewal, M. S., Weill, L., & Andrews, A. P. (2013). Global positioning systems, inertial navigation and integration. Hoboken, NJ: Wiley.
12- Hall,P,(2001). A Bayesian Approach to Map-Aided Vehicle Positioning,Forssell,U, Nordlund,P.J, Linköping University, Department of Electrical Engineering
13- Harvey, W. (1976) Use of the HARVEY procedure. SUGI Proceedings.
14- Kim, Y., & Bang, H. (2019). Introduction to Kalman Filter and Its Applications. Introduction and Implementations of the Kalman Filter. doi:10.5772/intechopen.80600
15- Kim, W., Jee, G., & Lee, J. (2004). Efficient use of digital road map in various positioning for ITS. IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062). doi:10.1109/plans.2000.838299
16- Krakiwsky, Harris & Wong, E., C., R. (1988). A Kalman filter for integrating dead reckoning, map matching and GPS positioning. IEEE PLANS 88.,Position Location and Navigation Symposium, Record. Navigation into the 21st Century.doi:10.1109/plans.1988.195464
17- Lahrech, A., Boucher, C., & Noyer, J. (2004). Fusion of GPS and odometer measurements for map-based vehicle navigation. 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT 04.doi:10.1109/icit.2004.1490202
18- Li, W., & Wang, J. (2013). Magnetic Sensors for Navigation Applications: An Overview. Journal of Navigation,67(2), 263-275. doi:10.1017/s0373463313000544
19- Liu, X., Sima, J., Huang, Y., Liu, X., & Zhang, P. (2016). A Simplified Kalman Filter for Integrated Navigation System with Low-Dynamic Movement. Mathematical Problems in Engineering, 2016, 1-9. doi:10.1155/2016/3528146
20- Ma, Z., Qiao, Y., Lee, B., & Fallon, E. (2013). Experimental evaluation of mobile phone sensors. 24th IET Irish Signals and Systems Conference (ISSC 2013). doi:10.1049/ic.2013.0047
21- Magnusson, N,Odenman, T,(2012). Improving absolute position estimates of an automotive vehicle using GPS in sensor fusion,Chalmers University of Technology,Division of signal processing and Biomedical Engineering
22- Martinek, R., Rzidky, J., Jaros, R., Bilik, P., & Ladrova, M. (2019). Least Mean Squares and Recursive Least Squares Algorithms for Total Harmonic Distortion Reduction Using Shunt Active Power Filter Control. Energies, 12(8), 1545. doi:10.3390/en12081545
23- Mohd-Yasin, F., Korman, C., & Nagel, D. (2001). Measurement of noise characteristics of MEMS accelerometers. 2001 International Semiconductor Device Research Symposium. Symposium Proceedings (Cat. No.01EX497). doi:10.1109/isdrs.2001.984472
24- Mohd-Yasin, F., Zaiyadi, N., Nagel, D., Ong, D., Korman, C., & Faidz, A. (2009). Noise and reliability measurement of a three-axis micro-accelerometer. Microelectronic Engineering, 86(4-6), 991-995. doi:10.1016/j.mee.2008.12.045
25- Mosavi, Sadeghian, & Saeidi, M.R., M., S. (2011). Increasing DGPS Navigation Accuracy using Kalman Filter Tuned by Genetic Algorithm, IJCSI International Journal of Computer Science Issues,8(6), 1694-0814
26- Moussa, M., Moussa, A., & El-Sheimy, N. (2019). Steering Angle Assisted Vehicular Navigation Using Portable Devices in GNSS-Denied
27- Najjar, M. E., & Bonnifait, P. (2005). A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering. Autonomous Robots, 19(2), 173-191. doi:10.1007/s10514-005-0609-1
28- Nasrollahi, S,Ghahramani, N(2010).Fusion of GPS Positioning and Speed in Information with Inertial Navigation System Data Using Kalman Filter to Increase the Accuracy of a Bird Object, Thirteenth Student Conference of Iranian Electrical Engineering,Tehran, Tarbiat Modares University
29- Park, S., Gil, M.-S., Im, H., & Moon, Y.-S. (2019). Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data. Sensors, 19(5), 1168. doi: 10.3390/s19051168
30- Passaro, V. M., Cuccovillo, A., Vaiani, L., Carlo, M. D., & Campanella, C. E. (2017). Gyroscope Technology and Applications: A Review in the Industrial Perspective. Sensors,17(10), 2284. doi:10.3390/s17102284
31- Rezaei, S., & Sengupta, R. (2007). Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization. IEEE Transactions On Control Systems Technology, 15(6), 1080-1088. doi: 10.1109/tcst.2006.886439
32- Rhudy, M. B., Salguero, R. A., & Holappa, K. (2017). A Kalman Filtering Tutorial for Undergraduate Students. International Journal of Computer Science & Engineering Survey, 08(01), 01–18. doi: 10.5121/ijcses.2017.8101
33- Simon, D. (2006). Optimal state estimation: Kalman, H∞, and nonlinear approaches. Hoboken, NJ: Wiley.
34- Toledo-Moreo, R., Zamora-Izquierdo, M., & Gomez-Skarmeta, A. (2006). IMM-EKF based Road Vehicle Navigation with Low Cost GPS/INS. 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. doi:10.1109/mfi.2006.265590
35- Vosoughi,Keighobadi,Faraji,H,J,J(2017).Design and Implementation of AHRS by using Kautz Function and Predictive Estimator with Euler’s Dynamic, Modares Mechanical Engineering,Vol. 17, No. 6, pp. 221-232, 2017 (in Persian)
36- Wang, J. (2007). Intelligent MEMS INS/GPS integration for land vehicle navigation(Unpublished master's thesis).
37- Wen, X., Liu, C., Huang, Z., Su, S., Guo, X., Zuo, Z., & Qu, H. (2019). A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation. Sensors, 19(6), 1340. doi: 10.3390/s19061340
38- Wood, J. S., & Zhang, S. (2018). Identification and Calculation of Horizontal Curves for Low-Volume Roadways Using Smartphone Sensors. Transportation Research Record: Journal of the Transportation Research Board,2672(39), 1-10. doi:10.1177/0361198118759005
39- =Xue, L., Jiang, C., Wang, L., Liu, J., & Yuan, W. (2015). Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal. Micromachines, 6(2), 266-280. doi:10.3390/mi6020266
40- Zhang, P., Gu, J., Milios, E., & Huynh, P. (2005). Navigation with IMU/GPS/digital compass with unscented Kalman filter. IEEE International Conference Mechatronics and Automation, 2005. doi:10.1109/icma.2005.1626777