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Journals / Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi / 2023 / Cilt: 15 - Sayı: 1

Improving Accuracy in Inertial Navigation Systems with Machine Learning

Makine Öğrenmesi İle Ataletsel Navigasyon Sistemlerinde Doğruluğun Geliştirilmesi

Fatih ŞAHİN
Faruk ULAMIŞ
Pages
286–296
Publication Date
2023-01-31
DOI
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AI Summary & Key Takeaways

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

ANS measurements made with the proposed method have been applied for different environments and it has been observed that it makes measurements with higher precision than standard ANS.

📌 3 Key Takeaways

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Objective & Hypothesis:Inertial navigation systems help various air, land and sea vehicles to find their positions by using the sensor data you receive from a unit that is usually configured as an Inertial Measurement Unit (ICU).
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Key Findings & Data:Recently, this technology has become wearable by integrating into the feet or various parts of the body, but the biggest disadvantage of these systems is that they create errors that increase over time due to the sensors used.
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Conclusion & Impact:ANS measurements made with the proposed method have been applied for different environments and it has been observed that it makes measurements with higher precision than standard ANS.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Ataletsel Algılama · Ataletsel Ölçüm Birimi · Ölü Hesaplama

Abstract

Inertial navigation systems help various air, land and sea vehicles to find their positions by using the sensor data you receive from a unit that is usually configured as an Inertial Measurement Unit (ICU). Recently, this technology has become wearable by integrating into the feet or various parts of the body, but the biggest disadvantage of these systems is that they create errors that increase over time due to the sensors used. Minimizing these errors is of great importance in terms of location accuracy. In inertial navigation systems (ANS) made with an inertial measurement unit mounted on the foot, the correct determination of the zero velocity detection (SHA) process is the most important factor reducing the measurement errors. In this study, long short-term memory (LSTM), a Recurrent Neural Network (RNN) method, was used to detect SHA more accurately. This method makes a binary classification for zero velocity detection using sensor data. ANS measurements made with the proposed method have been applied for different environments and it has been observed that it makes measurements with higher precision than standard ANS.

Özet

Ataletsel navigasyon sistemleri, genellikle Ataletsel Ölçüm Birimi(AÖB) olarak yapılandırılan bir birimden aldığı sensör verilerini kullanarak hava, kara ve deniz araçlarının konumlarını bulabilmesine yardımcı olmaktadır. Son dönemlerde bu teknoloji ayağa veya vücudun çeşitleri yerlerine entegre edilerek giyilebilir hale getirilmektedir, fakat bu sistemlerin en büyük dezavantajı kullanılan sensörler nedeniyle zamanla artan hatalar oluşturmalarıdır. Bu hataları minimize etmek konum doğruluğu açısından büyük önem taşımaktadır. Ayağa takılı ataletsel ölçüm birimi ile yapılan ataletsel navigasyon sistemlerinde (ANS), sıfır hız algılama (SHA) işleminin doğru tespit edilmesi ölçüm hatalarını düşüren en önemli etkendir. Bu çalışmada, SHA'yı daha doğru bir şekilde tespit etmek için Tekrarlayan Sinir Ağı (RNN) yöntemi olan uzun kısa süreli bellek (LSTM) kullanılmıştır. Bu yöntem sensör verilerini kullanarak sıfır hız algılama için ikili bir sınıflandırma yapmaktadır. Önerilen yöntemle yapılan ANS ölçümleri farklı ortamlar için uygulanmış ve standart ANS' den daha yüksek hassasiyette ölçümler yaptığı görülmüştür.

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Cite This Article

ŞAHİN, F., ULAMIŞ, F. (2023). Improving Accuracy in Inertial Navigation Systems with Machine Learning. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 15 - Sayı: 1, 286–296. https://ajindex.com/articles/889057

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