تخمین وضعیت و سمت پهپاد با استفاده از کواترنیون و فیلتر ذره‌ای

موذن, علی اصغر; هاونگی, رمضان

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشگاه بیرجند

چکیده

تخمین دقیق وضعیت و سمت پهپاد‌ها، به‌عنوان یکی از چالش‌های اصلی در سیستم‌های ناوبری خودکار، نقش حیاتی در کنترل و هدایت این وسایل ایفا می‌کند. در این مقاله، برای تخمین وضعیت پهپاد‌ها از فیلتر ذره‌ای مبتنی بر کواترنیون، یک روش نمونه‌برداری خاص و فیلتر کالمن بی‌اثر استفاده شده است. این روش‌ها با تلفیق داده‌های سنسورهای اینرسی و به‌کارگیری فیلترینگ، دقت تخمین زوایای غلت، فراز و سمت را به طور قابل‌توجهی افزایش می‌دهند. نوآوری این مقاله در مقایسه جامع عملکرد فیلتر ذره‌ای و فیلتر کالمن بی‌اثر در دو سناریوی مختلف، شامل داده‌های شبیه‌سازی‌شده و داده‌های واقعی است که از یک سیستم مرجع وضعیت و سمت خاص جمع‌آوری شده‌اند. نتایج نشان می‌دهند که فیلتر ذره‌ای در تخمین زوایای غلت و سمت بهبودی بیش از ۹۹٫۹۹٪ در داده‌های شبیه‌سازی‌شده و بیش از ۸۸٫۴۸٪ در داده‌های واقعی نشان داده است. همچنین، تحلیل معیارهای واریانس و انحراف استاندارد خطا تأیید می‌کند که فیلتر ذره‌ای در کاهش پراکندگی خطاها موفق‌تر عمل کرده است، به‌طوری‌که برای زاویه سمت، واریانس خطا در فیلتر ذره‌ای حدود ۱۰۰ برابر کمتر از فیلتر کالمن بی‌اثر بوده است. از سوی دیگر، فیلتر کالمن بی‌اثر در تخمین زاویه فراز عملکردی کمی بهتر از فیلتر ذره‌ای نشان داده است. این یافته‌ها حاکی از آن است که ترکیب این دو فیلتر می‌تواند به‌عنوان یک راه‌حل کارآمد در سیستم‌های ناوبری دقیق پهپادها مورد استفاده قرار گیرد. روش بازنمونه‌گیری به کار گرفته شده در فیلتر ذره‌ای نیز دقت تخمین زوایای غلت و سمت را به طور چشمگیری افزایش داده است.

کلیدواژه‌ها

20.1001.1.28211553.1404.4.2.3.3

موضوعات

مکانیک دفاعی/ناوبری/کنترل/...

عنوان مقاله [English]

Attitude and Heading Estimation of UAV Using Quaternion and Particle Filter

نویسندگان [English]

Aliasghar Moazzen
Ramazan Havangi

University of Birjand

چکیده [English]

Accurate estimation of attitude and heading in UAVs is one of the key challenges in autonomous navigation systems, playing a vital role in the control and guidance of these vehicles. In this paper, a quaternion-based particle filter with a specific sampling method and an extended Kalman filter (EKF) are employed for UAV attitude estimation. By integrating data from inertial sensors (including gyroscopes, accelerometers, and magnetometers) and applying filtering techniques, the proposed methods significantly enhance the accuracy of roll, pitch, and yaw angle estimation. The innovation of this study lies in the comprehensive comparison of the particle filter and EKF performance across two scenarios: simulated data and real-world data collected from a specific attitude and heading reference system (AHRS). The results demonstrate that the particle filter achieves remarkable improvements of over 99.99% in simulated data and over 88.48% in real-world data for roll and yaw angle estimation. Additionally, the analysis of variance and standard deviation of errors confirms that the particle filter outperforms in reducing error dispersion, with the error variance for yaw angle being approximately 100 times lower than that of the EKF. On the other hand, the EKF shows slightly better performance in pitch angle estimation. These findings suggest that a combination of these two filters can serve as an effective solution for precise navigation systems in UAVs. The resampling method implemented in the particle filter also significantly enhances the accuracy of roll and yaw angle estimation.

کلیدواژه‌ها [English]

State Estimation
Particle Filter
Quaternion
Inertial Navigation Systems
UAV

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مجله دفاع هوافضایی

تخمین وضعیت و سمت پهپاد با استفاده از کواترنیون و فیلتر ذره‌ای

مراجع

مراجع

دوره 4، شماره 2
شهریور 1404
صفحه 51-86

تخمین وضعیت و سمت پهپاد با استفاده از کواترنیون و فیلتر ذره‌ای

مراجع

مراجع

دوره 4، شماره 2شهریور 1404صفحه 51-86

دوره 4، شماره 2
شهریور 1404
صفحه 51-86