Geophysical Field Sensing Navigation Model Selection
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Solution Overview
Problem
Existing navigation systems face challenges in estimating geophysical fields for magnetic navigation, especially in environments where GPS is degraded, denied, or unavailable, due to the superposition of various magnetic fields from different sources.
Innovation Solution
The system implements a computer-implemented method that stores an offline baseline estimation model on a navigation object and selectively uses either this model or online geophysical field model data based on connectivity status, position, and time, to estimate geophysical fields for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If offline baseline estimation model is used for geophysical field estimation, then navigation functionality is maintained without base station infrastructure, but measurement precision of geophysical fields deteriorates compared to online models
Solution Approach 1:
The system pre-stores multiple baseline estimation models (e.g., IGRF, WMM, EMPCOM) on the navigation object before operation. These models are prepared in advance with different accuracy characteristics and computational requirements, enabling the system to function autonomously without real-time base station connectivity while maintaining navigation capability through offline geophysical field estimation.
Solution Approach 2:
The system dynamically selects between different baseline estimation models based on real-time conditions including connectivity status, position accuracy requirements, and computational resources available. This dynamic adaptation allows the navigation object to switch from higher-precision online models when connected to appropriate offline models when disconnected, resolving the contradiction between reliability and measurement precision.
2Measurement precision
If online geophysical field model data is used, then measurement precision of geophysical fields is improved, but navigation functionality deteriorates when data connectivity is degraded or unavailable
Solution Approach 1:
The system changes the parameter of model availability by maintaining multiple baseline estimation models with different accuracy levels and data requirements in storage. When online connectivity is available, the system utilizes online geophysical field model data for high-precision estimation. When connectivity degrades or becomes unavailable, the system transitions to using pre-stored offline baseline models, ensuring continuous navigation functionality despite the reduction in measurement precision.
3Adaptability or versatility
If multiple baseline estimation models are stored on navigation object, then adaptability to different navigation conditions is improved, but device complexity increases
Solution Approach 1:
The navigation object is designed with multi-functionality by storing multiple types of baseline estimation models (e.g., IGRF, WMM, EMPCOM) that can serve different navigation conditions and accuracy requirements. This universal approach allows a single navigation system to handle various operational scenarios including high-precision requirements, low-precision requirements, different geographic regions, and different connectivity states without requiring separate specialized systems.
Solution Approach 2:
The system incorporates automated control logic that independently selects the appropriate baseline estimation model based on current navigation conditions, position accuracy requirements, and available computational resources. This self-service mechanism eliminates the need for manual intervention in model selection, managing the complexity of multiple stored models through autonomous decision-making while maintaining adaptability to changing operational requirements.
4Measurement precision
If control logic continuously selects between offline and online models, then measurement precision is maintained through optimal model selection, but use of energy increases due to continuous connectivity status monitoring and model switching
Solution Approach 1:
Instead of continuous monitoring and model switching, the system implements periodic evaluation of connectivity status and navigation conditions. The control logic assesses whether to switch between offline and online baseline estimation models at predetermined intervals or when specific trigger conditions are met (e.g., connectivity loss, position accuracy thresholds). This periodic approach maintains measurement precision through timely model selection while significantly reducing energy consumption compared to continuous monitoring and switching operations.
Data Source
AI summary
Example computer-implemented methods and systems for estimating geophysical fields for magnetic navigation. One example computer-implemented method includes storing, at a navigation object, an offline baseline estimation model. Online geophysical field model data not stored on the navigation object are received at various times at the navigation object. Control logic is used to select at least one of (1) the offline baseline estimation model and (2) the online geophysical field model data to use to estimate geophysical fields for the navigation object at a variety of specified times.