GPS and Dead Reckoning Integrated Navigation System
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Solution Overview
Problem
Existing navigation systems face challenges in urban areas where GPS signals are obstructed, leading to inaccurate positioning due to signal obstruction and error accumulation in dead reckoning systems.
Innovation Solution
A GPS&DR integrated navigation system that combines GPS receivers with dead reckoning systems, using Kalman filters to integrate navigation information and adjust weight values based on signal reliability, thereby reducing errors and maintaining accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used for navigation in urban areas, then absolute position can be obtained with global reach and relatively high precision, but continuous navigation cannot be realized where satellite signals are obstructed by tall buildings, trees, tunnels, or seriously disturbed
Solution Approach 1:
The patent combines GPS and DR systems into an integrated navigation system. The filter processes both GPS navigation information and DR navigation information simultaneously, allowing the system to leverage the absolute positioning capability of GPS while maintaining continuous navigation through DR when GPS signals are obstructed in urban environments.
Solution Approach 2:
The filter acts as an intermediary that integrates GPS and DR navigation information. It calculates observation information by combining both sources according to weight values, and further integrates this with previous navigation information to produce current navigation information, thereby mediating between the two systems to overcome their individual limitations.
2Productivity
If DR navigation system is used for continuous navigation, then autonomous navigation with high sampling rate can be achieved, but error accumulates with time as absolute position is determined by adding relative displacement to previous positioning point
Solution Approach 1:
The filter implements feedback by integrating current DR navigation information with previous navigation information from multiple previous cycles. This feedback mechanism allows the system to continuously refine position estimates and correct DR error accumulation by referencing historical data and GPS updates when available.
Solution Approach 2:
The system merges DR's high sampling rate capability with GPS's absolute positioning accuracy. The filter combines DR navigation information (which provides continuous high-rate updates) with GPS navigation information (which provides accurate absolute references), thereby achieving both continuous navigation and reduced error accumulation.
3Duration of action of stationary object
If DR system calculates absolute position by adding relative displacement to previous positioning point, then continuous navigation is maintained, but error accumulates over time
Solution Approach 1:
The filter uses feedback by integrating observation information (derived from both GPS and DR) with previous navigation information from multiple previous cycles. This feedback loop continuously refines the position estimate, correcting DR error accumulation while maintaining continuous navigation capability.
Solution Approach 2:
The system dynamically adjusts weight values for GPS and DR navigation information based on their respective reliabilities. When GPS signals are available and reliable, higher weight is given to GPS; when GPS is obstructed, higher weight is given to DR. This parameter change strategy optimizes positioning accuracy while maintaining continuous navigation.
Data Source
AI summary
A global positioning system and dead reckoning (GPS&DR) integrated navigation system includes a GPS receiver coupled to a moving object for periodically generating GPS navigation information of said moving object, a DR system coupled to said moving object for periodically calculating DR navigation information of said moving object, and a filter coupled to said GPS receiver and said DR system for periodically calculating navigation information of said moving object, wherein said filter gets observation information by integrating said GPS navigation information and said DR navigation information according to a weight value of said GPS navigation information and a weight value of said DR navigation information, and calculates a current navigation information by integrating said observation information with previous navigation information from a plurality of previous cycles.