فیلتر ذره‌ای با مدل مشاهده مبتنی بر فیلتر وفقی کرنلی
محورهای موضوعی : مهندسی برق و کامپیوترحمیده حائری 1 * , هادی صدوقی یزدی 2
1 - مؤسسه آموزش عالی اقبال لاهوری مشهد
2 - دانشگاه فردوسی مشهد
کلید واژه: فیلتر ذره‌ای حداقل میانگین مربعات خطای کرنلی (KLMS) حداقل مربعات بازگشتی کرنلی (KRLS) تخمین مدل,
چکیده مقاله :
هرچند که فیلتر ذره‌ای ابزاری مؤثر در ردیابی شیء می‌باشد اما یکی از محدودیت‌های موجود، نیاز به وجود مدلی دقیق برای حالت سیستم و مشاهدات است. بنابراین یکی از زمینه‌های مورد علاقه محققین تخمین تابع مشاهده با توجه به داده‌های یادگیری است. تابع مشاهده ممکن است خطی یا غیر خطی در نظر گرفته شود. روش‌های موجود در تخمین تابع مشاهده با مشکلاتی مواجه هستند و از جمله این مشکلات، وابستگی به مقدار اولیه پارامترها در روش‌های دومرحله‌ای مبتنی بر ماکسیمم‌سازی انتظار و نیازمندی به یک سری مدل از پیش تعریف شده در روش‌های مبتنی بر چند مدل می‌باشد. در این مقاله، یک روش بدون راهنما برای غلبه بر این مشکلات با استفاده از فیلترهای وفقی کرنلی ارائه شده است. به این منظور از فیلترهای وفقی حداقل میانگین مربعات خطای کرنلی یا حداقل مربعات بازگشتی کرنلی برای تخمین تابع غیر خطی مشاهده استفاده می‌شود. با فرض معلوم‌بودن تابع فرایند و با داشتن دنباله‌ای از مشاهدات، تابع مشاهده مجهول تخمین زده می‌شود. ضمناً برای کاهش هزینه محاسباتی و افزایش سرعت اجرا، از روش تُنُک‌سازی داده‌ها با استفاده از روش وابستگی خطی تقریبی استفاده شده و الگوریتم پیشنهادی در دو کاربرد مورد ارزیابی قرار گرفته است. آزمایش اول بر پیش‌بینی سری‌های زمانی و دیگری روی ردیابی اشیا در ویدئو می‌باشد. نتایج به دست آمده حاکی از برتری روش پیشنهادی در مقایسه با چند روش موجود است.الگوریتم RRT به دست آمده و مقایسه گردید. این نتایج نشانگر کارایی مناسب رویکرد پیشنهادی است.
Particle filter is an effective tool for the object tracking problem. However, obtaining an accurate model for the system state and the observations is an essential requirement. Therefore, one of the areas of interest for the researchers is estimating the observation function according to the learning data. The observation function can be considered linear or nonlinear. The existing methods for estimating the observation function are faced some problems such as: 1) dependency to the initial value of parameters in expectation-maximization based methods and 2) requiring a set of predefined models for the multiple models based methods. In this paper, a new unsupervised method based on the kernel adaptive filters is presented to overcome the above mentioned problems. To do so, least mean squares/ recursive least squares adaptive filters are used to estimate the nonlinear observation function. Here, given the known process function and a sequence of observations, the unknown observation function is estimated. Moreover, to accelerate the algorithm and reduce the computational costs, a sparsification method based on approximate linear dependency is used. The proposed method is evaluated in two applications: time series forecasting and tracking objects in video. Results demonstrate the superiority of the proposed method compared with the existing algorithms.
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