About this Research Topic
Challenges and debates in navigation and positioning within contested environments persist in academic and industrial realms. This Research Topic carries significant potential in expansive commercial markets, including Intelligent Transportation Systems (ITS) and the Internet of Things (IoT), both now and in the future. Diverse methodologies aim to achieve seamless, accurate, and reliable navigation, encompassing absolute positioning via Global Navigation Satellite Systems (GNSS), Low Earth Orbit (LEO) satellites, wireless communication signals (e.g., 5G/LTE), radar signals, and other signals of opportunity. Additionally, relative positioning utilizes auxiliary sensors like inertial, LiDAR systems, cameras, magnetometers, barometers, etc.
Stochastic modelling of random errors is crucial in navigation applications. A precise stochastic model enhances the calibration performance of low-cost navigation devices. The effectiveness of real-time navigation systems is intricately linked to recursive filters. Improved navigation performance is attainable when the navigation filter employs a more effective stochastic model, especially in challenging scenarios like dense urban and indoor areas.
Effectively modelling stochastic errors in navigation signals and data from low-cost sensors/devices is imperative for current navigation systems. The traditional method, Allan Variance (AV) analysis, faces challenges in separating time-correlated random processes in the spectral domain, particularly when applied to low-cost navigation devices. In contrast, the Generalized Method of Wavelet Moments (GMWM) has recently gained recognition as a statistically consistent estimator, demonstrating asymptotic normality, computational efficiency, and the ability to handle the intricacies of high-complexity random error structures. Moreover, Machine Learning (ML) and Artificial Intelligence (AI) techniques have emerged as effective tools for stochastic modelling. This is attributed to their capability to capture and learn complex patterns, adapt to uncertainties, and make probabilistic predictions based on their capacity to handle non-linearity and high-dimensional data.
The complexity of data sources is noteworthy in current navigation systems, which often integrate diverse sensors and devices. Effective stochastic modelling for these systems is pivotal, ensuring rapid convergence for seamless navigation, preserving reliability, sustaining high-quality navigation, and positioning solutions, and mitigating degradation of navigation accuracy during GNSS outages.
Scope:
● Stochastic Modelling in Navigation Systems
● Signal Processing in Navigation and Positioning
● Navigation and Positioning Algorithms
Specific Themes:
● Stochastic Modelling in Navigation Sensor and System Signals
● Navigation, Positioning, and Wireless Localization
● Advanced Digital Signal Processing in Navigation
● Machine/Deep Learning for the Analysis of Navigation Data/Signals
Types of Manuscripts of Interest:
● Digital Signal Processing for Enhanced Wireless Localization (GNSS Signals, LEO Satellite Signals, and Other Signals of Opportunity)
● Advanced Baseband Processing for GNSS/LEO Satellite Signals
● Multi-Sensor Integrated Navigation Systems
● Navigation and Positioning Based on Signals of Opportunity (e.g., 5G/LTE, LEO, etc.)
● Navigation and Positioning Based on Low-Cost Devices like Smartphones
● Precise Positioning in Challenging Environments
● Advanced Navigation and Positioning Algorithms (e.g., FGO/EKF/UKF/PF, etc.)
● Improved Relative Positioning Based on Auxiliary Sensors (e.g., Inertial Sensors, LiDAR Systems, Cameras, Magnetometers, Barometers, etc.)
● Navigation and Localization with Radar Sensors (e.g., UWB Radar, MIMO Radar, SAR, etc.)
Stochastic modelling of random errors is crucial in navigation applications. A precise stochastic model enhances the calibration performance of low-cost navigation devices. The effectiveness of real-time navigation systems is intricately linked to recursive filters. Improved navigation performance is attainable when the navigation filter employs a more effective stochastic model, especially in challenging scenarios like dense urban and indoor areas.
Effectively modelling stochastic errors in navigation signals and data from low-cost sensors/devices is imperative for current navigation systems. The traditional method, Allan Variance (AV) analysis, faces challenges in separating time-correlated random processes in the spectral domain, particularly when applied to low-cost navigation devices. In contrast, the Generalized Method of Wavelet Moments (GMWM) has recently gained recognition as a statistically consistent estimator, demonstrating asymptotic normality, computational efficiency, and the ability to handle the intricacies of high-complexity random error structures. Moreover, Machine Learning (ML) and Artificial Intelligence (AI) techniques have emerged as effective tools for stochastic modelling. This is attributed to their capability to capture and learn complex patterns, adapt to uncertainties, and make probabilistic predictions based on their capacity to handle non-linearity and high-dimensional data.
The complexity of data sources is noteworthy in current navigation systems, which often integrate diverse sensors and devices. Effective stochastic modelling for these systems is pivotal, ensuring rapid convergence for seamless navigation, preserving reliability, sustaining high-quality navigation, and positioning solutions, and mitigating degradation of navigation accuracy during GNSS outages.
Scope:
● Stochastic Modelling in Navigation Systems
● Signal Processing in Navigation and Positioning
● Navigation and Positioning Algorithms
Specific Themes:
● Stochastic Modelling in Navigation Sensor and System Signals
● Navigation, Positioning, and Wireless Localization
● Advanced Digital Signal Processing in Navigation
● Machine/Deep Learning for the Analysis of Navigation Data/Signals
Types of Manuscripts of Interest:
● Digital Signal Processing for Enhanced Wireless Localization (GNSS Signals, LEO Satellite Signals, and Other Signals of Opportunity)
● Advanced Baseband Processing for GNSS/LEO Satellite Signals
● Multi-Sensor Integrated Navigation Systems
● Navigation and Positioning Based on Signals of Opportunity (e.g., 5G/LTE, LEO, etc.)
● Navigation and Positioning Based on Low-Cost Devices like Smartphones
● Precise Positioning in Challenging Environments
● Advanced Navigation and Positioning Algorithms (e.g., FGO/EKF/UKF/PF, etc.)
● Improved Relative Positioning Based on Auxiliary Sensors (e.g., Inertial Sensors, LiDAR Systems, Cameras, Magnetometers, Barometers, etc.)
● Navigation and Localization with Radar Sensors (e.g., UWB Radar, MIMO Radar, SAR, etc.)
Keywords: Navigation systems, Stochastic modelling, GMWM, Wireless Localization, Positioning
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