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Distributed Kalman Filtering

l Distributed Kalman Filtering. PhD thesis, King Fahd University of Petroleum and Minerals.

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Arabic Abstract

تزايدت فى الآونه الاخيرة الحاجه الملحه لاستيعاب التاثيرات المتعددة لهياكل المعلومات الموزعه على ترشيح الاشارات و تقديراتها. ومن هذا المنطلق تفرد هذه الاطروحه لدراسه متعمقه "ترشيحات كالمان الموزعه" من جوانب متنوعه. وبادئ ذى بدء تقدم الاطروحه عرض ببلوجرافىمستفيض للمراجع والبحوث المنشوره والمتداوله حيث تم ابراز تقسيمات فنيه للطرائق واساليب البحث ثم استعرضت التطبيقات المتنوعة وأجرت دراسه مقارنه متعدده الجوانب بين البحوث. وتنطلق الاطروحه لتناقش موضوع هام يختص بالتقديرات الموزعه التقريبيه من خلال الهياكل الحاكمه للشبكات الموزعه ومستخدمه لاسلوب "باى" ذو الاتجاهين مع منظور عام لمرشح كالمان. ويفرد هذا الفصل للمعالجات الرياضيه التحليليه فى حالات توافر كامل للمعلومات أو نوافر جزئى للمعلومات أو غياب المعلومات مع تبيان دور تدفق المعلومات و معدلاتها. وينظر الفصل التالى فى موضوع تعظيم التوقعات الموزعه بالاستناد الى صور مصغره ل"منعم كالمان المتصل المفرد" ويقدم الفصل منعمات مثاليه مصغره لتقويم الحاله الديناميكيه للنظم. ثم تتناول الاطروحه بعد ذلك موضوع مرشح مصفوفه المعلومات حيث تستنبط خوارزم جديد للتقديرات الموزعه من خلال نظام تتبع موزع ذو كفاءه عاليه و تتوافق فيه مجموعه محدده من المشعرات لتحديد هدف واحد بناءا على التقاطع المثمر للتباين المشترك . وقد تم أختبار أداء الخوارزم فى ضوء وسائل انبعاثيه متعدده. وقد أفردت الاطروحه فصول خاصه للمحاكاه على نماذج لعمليات عمليه و صناعيه تشمل نظام كهروهيدروديناميكى و تظام نوعى للطاقه الكهربيه و مراجل صناعيه . وبينت النتائج مدى فعاليه النظم المستنبطه فى الاطروحه.

English Abstract

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this thesis, distributed Kalman filtering has been on focus with various perspectives. Firstly, a bibliographical review on distributed Kalman filtering (DKF) is provided. A classification of different approaches and methods involved to DKF has been elaborated, followed by the applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical application of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area. Secondly, an approximate distributed estimation within distributed networked control formalism has been proposed. This is made possible by using Bayesian-based forward-backward (FB) system with generalized versions of Kalman filter. The analytical treatment is presented for cases with complete, incomplete or no prior information with bounds and then followed by estimation fusion for all three cases. The proposed scheme is validated on a rotational drive-based electro-hydraulic system and the ensuing results ensured the effectiveness of the scheme underpinning it. The thesis proposes distributed expectation maximization (EM)-based reduced-order singular evolutive extended Kalman (SEEK) smoother. Optimal reduced-order smoothers complement the computation by doing re-analysis to correct the state of a dynamic system. The nature of order reduction of the SEEK smoother is fulfilling this phase, and made more precise by injecting the Kalman-like particle nature of the filter. The proposed scheme is first evaluated with its distributed full-order EM-based smoother version, followed by its reduced order version. The EM algorithm plays its role to identify and improve the estimate of process noise covariance Q in each case. The proposed scheme is then validated on a power quality system with various kinds of loads, ensuring the effectiveness and applicability of the scheme underpinning it. An approach for distributed estimation algorithm is proposed using information matrix filter on a distributed tracking system in which N number of sensors are tracking the same target. The approach incorporates proposed engineered versions of information matrix filter derived from covariance intersection, weighted covariance and Kalman-like particle filter (KLPF) respectively. The steady performance of these filters is evaluated with different feedback strategies, moreover employing them with commonly used measurement fusion methods i.e. measurement fusion and state-vector fusion respectively to complete the picture. The proposed filters are then validated on an industrial utility boiler, ensuring the effectiveness and applicability of the scheme underpinning it.

Item Type:Thesis (PhD)
Research > Engineering
Divisions:College Of Computer Sciences and Engineering > Systems Engineering Dept
Committee Advisor:Mahmoud, Magdi S.
Committee Members:Shafei, Moustafa and Selim, Shukri and Saif, Abdul Wahid and Abido, Mohammad Ali
ID Code:138865
Deposited By:Haris M. Khalid Haris M. Khalid
Deposited On:01 Apr 2013 09:10
Last Modified:08 Apr 2013 09:10

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