فیلترذره مبتنی برMCMC به منظور ردگیری چندهدفه در میان مشاهدات خام و آشکارنشده

نویسنده

پردازش

چکیده

در این مقاله به مسئله پرچالش ردگیری چندهدفه در میان داده­های آشکارنشده پرداخته می­شود. برای انجام این کار، ابتدا با تقسیم فضای حالت به دو زیر فضای خطی و غیرخطی و با به­کارگیری اصل Rao–Blackwellization، چگالی اهمیتی بهینه را برای نوع خاصی از مدل سنسور، که مشاهدات منشعب و در هم ادغام­شده را برای ناحیه مشاهده مشبک­شده تولید می­نماید، به­دست آمد. در ادامه، برای کاهش پیچیدگی محاسباتی نمونه برداری از چگالی اهمیتی بهینه، از معروف­ترین نمونه­بردار خانواده MCMC یعنی نمونه­بردار Gibbs برای نمونه­برداری از چگالی اهمیتی بهینه استفاده شد و سپس با مقایسه عملکرد این دو در یک محیط ردگیری چندهدفه و در میان مشاهدات خام و آشکارنشده، نشان داده شد که نمونه­بردار Gibbs به مبادله­ای بین کاهش حجم محاسبات و میزان دقت در ردگیری دست می­یابد. ایده مطرح­شده را می­توان به­عنوان جایگزین برای مواقعی که نمونه­برداری از چگالی اهمیتی بهینه عملا غیرممکن است، استفاده نمود.

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