بهبود خطای RMS موقعیت و سرعت فیلترهای غیرخطی در ردگیری راداری ماهواره‌های LEO

نوع مقاله: مقالة‌ تحقیقی‌ (پژوهشی‌)

نویسندگان

1 دانشکدة مهندسی فناوری اطلاعات و ارتباطات، دانشگاه جامع امام حسین (ع)

2 دانشکدة مهندسی برق، دانشگاه صنعتی شریف

چکیده

از میان روش­های مختلف شناسایی و ردگیری ماهواره­های مدار پایین زمین (LEO)، روش ردگیری راداری مناسب­تر است. ازآنجاکه فیلترهای خطی مرسوم، قادر به تخمین موقعیت و ردگیری دقیق سیستم­های با دینامیک غیرخطی مثل ماهواره­ها نیستند، باید از فیلترهای غیرخطی استفاده کرد. در این مقاله، ابتدا مسیر حرکت ماهواره به دور زمین با استفاده از معادلات حرکتی ماهواره (معادلات کاول) و به تبع آن مشاهدات رادار شبیه‌سازی شده و جهت تخمین موقعیت و ردگیری به فیلترهای غیرخطی کالمن توسعه­یافته (EKF) و کالمن نمونه­بردار (UKF) اعمال می­شوند. در مرحلۀ بعد برای کاهش خطای تخمین، از دیتای تولیدی در نرم­افزار STK استفاده کرده و در نهایت به بررسی خطای RMS موقعیت و سرعت و همچنین خطای تخمین هر یک از فیلترها در دو روش می­پردازیم. نتایج شبیه­سازی نشان می­دهد که به دلیل دقت بیشتر مسیر حرکت تولیدی در STK، فیلترها، در این روش تخمین بهتری زده و حداکثر خطای RMS موقعیت در حدود 40 درصد کاهش می­یابد.

کلیدواژه‌ها


عنوان مقاله [English]

Reduce Position and Velocity RMS Error of Non-linear Filters in LEO Satellite Radar Tracking

نویسندگان [English]

  • Javad Salem 1
  • hossein pilaram 2
  • seyyed mohammad alavi 1
1 Department of Information Technology Engineering, Imam Hossein University
2 Department of Electrical Engineering, Sharif University of Technology
چکیده [English]

For the detection of and tracking thelow earth orbit Satellites (LEO), there are different methods such as optic, laser and radar tracking, among which radar tracking is the best. Since the common linear tracking filters deployed in available radars are not able to estimate the position of the non-linear dynamic satellites, it is advisable to use non-linear filters. In this paper, firstly, the satellite motion path around the earth as well as radar observations are produced by the STK software. Accordingly, the samples are fed to non-linear Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Finally, the performance of the aforementioned filters is studied through evaluation of RMS position and estimation errors. Simulation results demonstrate that the Unscented Kalman filter has a better performance in terms of accuracy with respect to the Extended Kalman filter. In addition, using this method, theerror of observations decreases 50% along the range and 70% along the azimuth and elevation.

کلیدواژه‌ها [English]

  • LEO satellites
  • Radar observation
  • Extended kalman Filter (EKF)
  • Unscented kalman Filter (UKF)
  • STK software

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