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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2019 / Cilt: 35 Sayı: 1

Scalar-weight interacting multiple model based on converted measurements Kalman filter

Dönüştürülmüş ölçümler Kalman filtresi tabanlı skalerle ağırlıklandırılmış etkileşimli çoklu model

Kübra Turgut(GEBZE TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0001-8326-1019
Ali Köksal Hocaoğlu(GEBZE TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-0701-2787
Pages
71–80
Publication Date
2019-10-25
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AI Summary & Key Takeaways

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

It is shown that the proposed algorithm has smaller estimation error compared to SIMM-KF and IMM-CMKF algorithms.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we take advantage of the fusion criteria based- Scalar-weight Interacting Multiple Model (SIMM) algorithm and the Converted Measurements Kalman Filter (CMKF), which reduces the bias caused by coordinate transformations to propose a novel Interacting Multiple Model (IMM) tracking algorithm which uses multiple motion models for target tracking, The proposed algorithm has been tested on scenarios with highly maneuvering targets under heavy measurement noise.
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Key Findings & Data:In this study, we take advantage of the fusion criteria based- Scalar-weight Interacting Multiple Model (SIMM) algorithm and the Converted Measurements Kalman Filter (CMKF), which reduces the bias caused by coordinate transformations to propose a novel Interacting Multiple Model (IMM) tracking algorithm which uses multiple motion models for target tracking, The proposed algorithm has been tested on scenarios with highly maneuvering targets under heavy measurement noise.
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Conclusion & Impact:It is shown that the proposed algorithm has smaller estimation error compared to SIMM-KF and IMM-CMKF algorithms.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hedef takibi · etkileşimli çoklu model (IMM) · skalerle ağırlıklandırılmış ektileşimli çoklu model (SIMM)

Abstract

In this study, we take advantage of the fusion criteria based- Scalar-weight Interacting Multiple Model (SIMM) algorithm and the Converted Measurements Kalman Filter (CMKF), which reduces the bias caused by coordinate transformations to propose a novel Interacting Multiple Model (IMM) tracking algorithm which uses multiple motion models for target tracking, The proposed algorithm has been tested on scenarios with highly maneuvering targets under heavy measurement noise. It is shown that the proposed algorithm has smaller estimation error compared to SIMM-KF and IMM-CMKF algorithms.

Özet

Bu çalışmada hedef takibi için, füzyon kriterlerine dayalı Skalerle ağırlıklandırılmış Etkileşimli Çoklu Model (SIMM) algoritması ile koordinat dönüşümlerinden kaynaklı sapmanın azaltılmasına yönelik öne sürülen Dönüştürülmüş Ölçümler Kalman Filtresi (CMKF) algoritması özelliklerinden yararlanılarak birden fazla hedef hareket modelinin kullanımına olanak sağlayan yeni bir Etkileşimli Çoklu Model (IMM) algoritması önerilmiştir. Önerilen algoritma yoğun manevralı ve yoğun gürültülü senaryolarda test edilmiştir. Önerilen algoritmanın, ölçümlerin polar/küresel koordinat olması durumunda literatürdeki SIMM-KF ve IMM-CMKF algoritmalarından daha az mesafe hatasına sahip olduğu gösterilmiştir.

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Cite This Article

Turgut, K., Hocaoğlu, A. K. (2019). Scalar-weight interacting multiple model based on converted measurements Kalman filter. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 35 Sayı: 1, 71–80. https://doi.org/10.17341/gazimmfd.439384

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