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
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