Navigation System Using Gravity and Terrain Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Terrain Referenced Navigation (TRN) systems face challenges in maintaining accurate navigation solutions over flat terrain or in areas with gravity anomalies, as they rely on terrain features and radar altimeter data, and are prone to errors due to uncorrected inertial navigation system (INS) sensor measurements.
Innovation Solution
A navigation system that integrates an inertial navigation system with a terrain-based navigation unit and a gravity-based navigation unit, using an iterative algorithm to determine and update the INS error state, incorporating gravity information to correct position estimates and constrain error growth, thereby improving accuracy across various terrains and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Terrain Referenced Navigation (TRN) systems use radar altimeter data and digital terrain elevation data to determine navigation solution, then navigation accuracy is improved over feature-rich terrain, but the system fails to maintain accurate navigation over flat terrain or water where terrain features are absent
Solution Approach 1:
The patent combines TRN with Gravity Referenced Navigation (GRN) to create an integrated navigation system. The INS error state is updated using both terrain-based measurements and gravity-based measurements from a gravity gradiometer, allowing the system to maintain accuracy across diverse terrains including flat areas and water where traditional TRN fails.
Solution Approach 2:
The navigation system is designed to operate universally across different terrain types by incorporating multiple navigation methods. The system can switch between or combine TRN and GRN approaches depending on terrain characteristics, making it adaptable to mountains, flat land, and water surfaces.
2Productivity
If Inertial Navigation System (INS) integrates accelerometer and gyroscope outputs over time to determine position, then continuous navigation data is provided, but errors accumulate at a rate of around 2 nautical miles per hour leading to significant position errors
Solution Approach 1:
The patent implements feedback mechanisms where the INS error state is continuously updated using measurements from both TRN and GRN systems. This feedback loop corrects the accumulating INS errors by comparing predicted positions with actual positions derived from terrain and gravity measurements, thereby maintaining long-term accuracy.
Solution Approach 2:
The system performs preliminary error characterization by modeling INS error sources and propagating these errors through the navigation solution. By anticipating error accumulation patterns, the system can apply corrective updates more effectively before errors become significant.
3Measurement precision
If TRN systems rely on terrain features for navigation solution, then accurate positioning is achieved over feature-rich terrain, but the system becomes unsafe to use over flat terrain or water where no terrain features exist
Solution Approach 1:
The patent merges TRN and GRN systems to create a hybrid navigation solution. When terrain features are available, TRN provides accurate positioning. When terrain features are absent (flat terrain or water), the GRN system using gravity gradiometer measurements takes over, ensuring continuous reliable operation across all environments.
4Measurement precision
If the system integrates multiple navigation data sources (INS, TRN, GRN) with different uncertainty levels, then navigation accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent manages complexity by dynamically adjusting the weighting of different navigation data sources based on their current uncertainty levels. The Kalman filter automatically adjusts parameters such as measurement noise covariance matrices based on terrain characteristics and signal quality, optimizing the contribution of each sensor without requiring complex manual configuration.
Data Source
AI summary
A navigation system comprising: an inertial navigation system arranged to output a first position estimate; a terrain based navigation unit arranged to output a second position estimate; a gravity based navigation unit arranged to output a third position estimate; a stored gravity map arranged to receive a position and to output gravity information for that position; and an iterative algorithm unit arranged to determine an INS error state in each iteration; wherein in each iteration the iterative algorithm unit is arranged to: receive the first position estimate, the second position estimate, and the third position estimate; determine a gravity corrected position estimate based on the first position estimate, the INS error state and the gravity information; and update the INS error state for the next iteration based on the INS error state, the gravity corrected position estimate, the second position estimate and the third position estimate.