فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

تعیین فاکتور ثانویه اضافی سامانه ناوبری زمین پایه LPS بر مبنای شبکه یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان
آرمان صابری 1
نعمت الله قهرمانی 2
سعید نصراللهی 3
محمد علی کیوان راد 4
1 دانشجوی دکتری برق-کنترل، گروه کنترل، دانشکده برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران
2 دانشیارگروه کنترل، دانشکده برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران
3 استادیارگروه کنترل، دانشکده برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران
4 استادیار گروه هوش مصنوعی، دانشکده برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران
10.22131/sepehr.2025.2049922.3118
چکیده
سامانه‌ های ناوبری زمین‌پایه لورن به دلیل تأثیرپذیری از عوامل محیطی مانند ارتفاع، پوشش اراضی، هدایت الکتریکی، رسانش حرارتی، دما و رطوبت، با چالش‌های مهمی در دقت و پایداری مواجه هستند. این عوامل با تأثیر بر مسیر و شدت امواج الکترومغناطیسی، خطای فاکتور ثانویه اضافی را ایجاد می‌کنند که منجر به انحراف سیگنال می‌شود. در این مقاله، از روش تفاضل محدود در حوزه زمان برای محاسبه مقادیر فاکتور ثانویه اضافی در منطقه مطالعاتی شمال‌غرب ایران و در 17 کلاس مختلف پوشش اراضی استفاده شد. از آنجا که داده‌های هواشناسی محدود به نُه ایستگاه با مختصات معلوم بودند، برای پیش‌بینی پارامترهای جوّی کل منطقه، از تصاویر ماهواره‌ای مادیس بهره گرفته شد. مدل‌های یادگیری عمیق شبکه عصبی بازگشتی حافظه طولانی کوتاه‌مدت برای مدل‌سازی دقیق‌تر توزیع مکانی-زمانی دما به کار رفتند و توزیع دما در شبانه‌روز و نقاط مختلف منطقه مطالعاتی را با دقت بالایی پیش‌بینی کردند. یافته‌ها نشان داد که ارتفاعات بالا، زمین‌های با رسانایی الکتریکی و حرارتی بالا و پوشش‌های گیاهی متراکم، منجر به افزایش مقادیر فاکتور ثانویه اضافی می‌شوند. در مقابل، مناطق خشک و کم‌رسانا مقادیر کمتری از این خطا را تولید می‌کنند. بر اساس نتایج حاصله،ترکیب داده‌های ایستگاه‌های هواشناسی و تصاویر ماهواره‌ای، دقت قابل‌توجهی در تحلیل و پیش‌بینی تغییرات فاکتور ثانویه اضافی فراهم کرده است. علاوه بر این، تحلیل نتایج نشان داد که ارتباط مستقیمی میان دما و سایر پارامترهای هواشناسی با مقدار فاکتور ثانویه اضافی وجود دارد. پژوهش حاضر با ترکیب روش‌های عددی و یادگیری عمیق، راهکاری جامع برای تحلیل دقیق اثرات محیطی و جغرافیایی بر سامانه‌ی ناوبری زمین‌پایه ارائه داده و امکان بهبود دقت و کارایی این سامانه‌ها را در شرایط پیچیده جغرافیایی و جوّی فراهم می‌کند.
کلیدواژه‌ها
فاکتور ثانویه اضافی
سامانه ناوبری لورن
تعیین موقعیت
شبکه یادگیری عمیق
خطای اختلاف زمانی
معادلات تفاضل محدود حوزه زمان

موضوعات

عنوان مقاله English

Determination of the Additional Secondary Factor (ASF) of the ground-based navigation system (LPS) based on deep learning network

نویسندگان English

Arman Saberi 1
Nemat Allah Ghahremani 2
Saeid Nasrollahi 3
Mohammad ali Keyvanrad 4
1 Ph.D Candidate of control engineering,, Faculty of electrical and computer engineering,, Malek Ashtar University of Technology, Tehran, Iran
2 Associate professor of control engineering,, Faculty of electrical and computer engineering,, Malek Ashtar University of Technology, Tehran, Iran
3 Assistant professor of control engineering,, Faculty of electrical and computer engineering,, Malek Ashtar University of Technology, Tehran, Iran
4 Assistant professor of artificial intelligence, Faculty of electrical and computer engineering, Malek Ashtar University of Technology, Tehran, Iran
چکیده English

Extended Abstract
Introduction
   The Loran terrestrial navigation system is recognized as one of the key systems for providing precise positioning services and improving navigation performance on a global scale. These systems transmit low-frequency (LF) electromagnetic waves to deliver highly accurate timing signals to ground users, enabling access to high-precision positioning data. However, the performance of these systems is significantly influenced by environmental and geographical factors, including land cover, elevation, surface temperature, humidity, electrical conductivity, and thermal conductivity. These factors can introduce an additional secondary factor (ASF) error, causing changes in the path and intensity of electromagnetic waves, which consequently reduce the accuracy and reliability of signals. The importance of analyzing these factors and developing models to mitigate their effects is particularly critical in areas with diverse geographical characteristics. Additionally, the complexity of environmental factors such as topographical and climatic variations necessitates advanced methods for modeling and analyzing their impacts. In this context, combining numerical methods with deep learning has emerged as an effective solution to reduce errors arising from these factors and improve the performance of terrestrial navigation systems.
Materials and Methods
1. Numerical Modeling Using the FDTD Method
   The finite-difference time-domain (FDTD) method is a powerful numerical approach for simulating electromagnetic wave propagation under various environmental conditions. By discretizing Maxwell's equations, this method effectively models the interaction between electromagnetic waves and complex environmental features. In this study, the research area includes 17 distinct land cover classes such as dense forests, water bodies, and arid regions, enabling a comprehensive analysis of the environmental influences on ASF values. This modeling enhances our understanding of the interaction between electromagnetic waves and environmental conditions, paving the way for more accurate solutions to reduce errors stemming from these influences.
2. Integration of Meteorological Data
   One of the main challenges in this study was the limited availability of meteorological data from weather stations. To address this issue, data from nine weather stations in the study area were integrated with MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery. Satellite data, with its extensive spatial coverage and high temporal resolution, provides more accurate inputs for predicting environmental parameter changes. Variables such as temperature, humidity, and air pressure were processed and combined to enhance the quality of the model inputs. This data integration not only increased spatial and temporal resolution but also improved the accuracy of ASF predictions.
3. Deep Learning Using LSTM Networks
   Long short-term memory (LSTM) networks, a type of advanced deep learning model, are particularly well-suited for analyzing sequential data. In this study, LSTM networks were employed to predict the spatiotemporal distribution of meteorological parameters. These networks, by identifying long-term temporal dependencies in the data, significantly improved the accuracy of ASF predictions. The integration of inputs derived from the FDTD method and meteorological information provided a robust framework for analyzing the nonlinear relationships among environmental variables.
Results and Discussion
Key Findings
1- Impact of Land Cover: The results revealed that areas with dense vegetation and high electrical and thermal conductivity exhibited higher ASF values. This can be attributed to the significant influence of these land covers on the absorption and reflection of electromagnetic waves. Conversely, arid regions with low conductivity showed lower ASF values.
2- Impact of Elevation: Higher elevations were associated with increased ASF values. This increase is due to variations in atmospheric pressure and temperature at these elevations, which affect the path and intensity of electromagnetic waves.
3- Meteorological Relationships: The findings demonstrated a direct relationship between temperature, humidity, and ASF values. Warmer and more humid conditions amplified ASF errors, while cooler and drier conditions reduced these errors.
4- Temporal Variations: ASF values exhibited daily and seasonal variations, influenced by fluctuations in temperature and humidity over time. These variations necessitate periodic adjustments to maintain the accuracy of navigation systems.
Integration of Numerical Modeling and Deep Learning
   The results of this study demonstrated that combining the FDTD method with LSTM-based deep learning models provides an effective approach for mitigating ASF errors. The FDTD method accurately simulated complex environmental conditions, while the LSTM model identified long-term temporal patterns. This combination significantly enhanced the reliability of terrestrial navigation systems and reduced errors arising from environmental conditions.
Conclusion
   This study successfully integrated numerical modeling and deep learning to address the challenges associated with ASF errors in terrestrial navigation systems. The findings clearly highlighted the importance of considering environmental factors such as elevation, land cover, and meteorological conditions in improving the accuracy and performance of these systems. The proposed combined approach not only ensures the reduction of errors due to environmental conditions but also lays a foundation for future research in navigation and geospatial analyses.

کلیدواژه‌ها English

Additional Secondary Factor
Loran navigation system
Positioning
Deep learning network
Time difference error
Finite-difference time-domain method
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