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Journals / Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 13 Sayı: 1

Adaptive Unscented Kalman Filter

Uyarlı Kokusuz Kalman Filtresi

Esin Köksal Babacan
Levent Özbek
Cenker Biçer
Pages
59–67
Publication Date
2011-06-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, we propose a new adaptive Unscented Kalman Filter to compensate the effects of incomplete information and the estimation results are evaluated with a simulation study

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The well known conventional Kalman Filter requires an accurate system model and exact stochastic information.
📊
Key Findings & Data:However, in a number of practical situations, the system information is partly known or incorrect.
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Conclusion & Impact:In this study, we propose a new adaptive Unscented Kalman Filter to compensate the effects of incomplete information and the estimation results are evaluated with a simulation study
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kokusuz dönüşüm · Kokusuz Kalman Filtresi · Uyarlı Kalman Filtresi

Abstract

The well known conventional Kalman Filter requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system information is partly known or incorrect. In this situation, the filter may diverge or give based estimate. To overcome the problem of divergence, it is necesary to made some changes and robustness in filtering process. Here arises the notion of adaptive filter. In this study, we propose a new adaptive Unscented Kalman Filter to compensate the effects of incomplete information and the estimation results are evaluated with a simulation study

Özet

Bilinen Geleneksel Kalman Filtresi doğru bir sistem modeli ve tam stokastik bilgi gerektirir. Fakat, pek çok gerçek uygulamada sistem bilgisi tam olarak bilinmez veya yanlış bilinir. Bu nedenle filtre ıraksayabilir veya yanlı tahminler elde edilebilir. Bu ıraksama probleminin giderilebilmesi için filtreleme işleminde bazı değişikliklerin ve güçlendirmelerin yapılması gerekir. Uyarlı filtre kavramı burada ortaya çıkar. Bu çalışmada, lineer olmayan durum-uzay modellerinde, eksik sistem bilgisinden meydana gelebilecek problemlerin üstesinden gelebilmek için yeni bir Uyarlı Kokusuz Kalman Filtresi önerilmiş ve bir simülasyon çalışmasıyla tahmin sonuçları değerlendirilmiştir

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Cite This Article

Babacan, E. K., Özbek, L., Biçer, C. (2011). Adaptive Unscented Kalman Filter. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 13 Sayı: 1, 59–67. https://ajindex.com/articles/1263947

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References (8)

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  5. Almagbile, A., Wang, J. and Ding, W. (2010). Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration, Journal of Global Positioning Systems, Vol.9, No.1:33-40.
  6. Özbek, Levent; Köksal Babacan, Esin; Efe, Murat. Stochastic Stability of the Discrete-Time Constrained Extended Kalman Filter, Turk J. Elec. Eng. & Comp. Sci, Tübitak, Vol.18, No.2, 2010
  7. Chui, C. K. and Chen, G. (1991), Kalman Filtering with Real-time Applications, Springer-Verlag.
  8. Almagbile, A., Wang, J. and Ding, W. (2010). Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration, Journal of Global Positioning Systems, Vol.9, No.1:33-40.