دانلود رایگان مقاله موقعیت وابسته به وسایط نقلیه با GPS / IMU با کنترل تطبیقی کوواریانس فیلتر نویز

عنوان فارسی
یک موقعیت وابسته به وسایط نقلیه با GPS / IMU با استفاده از کنترل تطبیقی کوواریانس فیلتر نویز
عنوان انگلیسی
A vehicular positioning with GPS/IMU using adaptive control of filter noise covariance
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
6
سال انتشار
2016
فرمت مقاله انگلیسی
PDF
نشریه
الزویر - Elsevier
کد محصول
E3633
دانشگاه
مهندسی الکترونیک و کامپیوتر، دانشگاه هانیانگ، سئول، کره جنوبی
رشته های مرتبط با این مقاله
مهندسی فناوری اطلاعات و ارتباطات
کلمات کلیدی
GPS، جنبش اسلامی ازبکستان، تمدید فیلتر کالمن. سیستم / اندازه گیری کوواریانس نویس، موقعیت فضایی
گرایش های مرتبط با این مقاله
مخابرات سیار (موبایل)
مجله
فناوری اطلاعات و ارتباطات سریع - ICT Express
چکیده

Abstract

 

Vehicular positioning with GPS/IMU has been studied a lot to increase positioning accuracy. The positioning algorithms mainly use DR (Dead Reckoning) which uses EKF (Extended Kalman Filter). It is basic and very important core technology in positioning section. However, EKF has a major drawback in that it is impossible to make very accurate system and measurement models for a real environment. In this work, we propose an algorithm to estimate vehicle’s position as distribution form, and to control the system and measurement noise covariance to compensate for this major disadvantage. The proposed method to control noise covariance is independently processed, using fading factor and sensor error while considering the driving condition.

نتیجه گیری

5. Conclusion

 

In this paper, we proposed the method controlling EKF filter noise covariance which consisted of system and measurement noise covariance. They are independently, automatically adjusted by many factors. A system noise covariance Q indirectly influences the performance of system model in EKF by using Lamda λ. A measurement noise covariance R is adjusted by the driving conditions: the driving environments, the driving state. The result of positioning by using the proposed method is more reliable and accurate than only using EKF. In special situation, the performance of the proposed method is even better than the expensive instrument using high-cost RTK. In low-multipath area where RTK has quite good performance, the performance of the proposed algorithm is around 0.4 m better than one using only EKF.


بدون دیدگاه