ijerad International Journal of Engineering Research and Development 1308-5506 1308-5514 Kirikkale University 10.29137/umagd.1129097 Electrical Engineering Elektrik Mühendisliği Improving Accuracy in Inertial Navigation Systems with Machine Learning Makine Öğrenmesi İle Ataletsel Navigasyon Sistemlerinde Doğruluğun Geliştirilmesi https://orcid.org/0000-0001-5090-3679 Şahin Fatih KIRIKKALE ÜNİVERSİTESİ https://orcid.org/0000-0002-7863-755X Ulamış Faruk KIRIKKALE ÜNİVERSİTESİ 01 31 2023 15 1 286 296 06 10 2022 01 31 2023 Copyright © 2009, International Journal of Engineering Research and Development 2009 International Journal of Engineering Research and Development

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.

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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