Real-Time INS/GNSS Navigation Module With Delayed Signal Handling
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Solution Overview
Problem
Current real-time navigation systems face challenges in accurately integrating GNSS and INS data due to delays in GNSS information reception, which disrupts the timely update process, leading to inaccuracies in positioning and navigation solutions, especially in environments with degraded or denied satellite signals.
Innovation Solution
A navigation module that integrates GNSS and INS data in real-time using a processor to handle delayed GNSS information by buffering high-rate sensor data and performing prediction phases independently, allowing for continuous and accurate navigation outputs despite GNSS delays, utilizing filtering techniques like Kalman filters for state estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS information is integrated with INS data in real-time, then positioning accuracy is improved, but system reliability deteriorates due to signal blockage and delays in degraded or denied environments
Solution Approach 1:
The system segments the positioning function into two independent components: GNSS-based absolute positioning and INS-based relative positioning. This allows the system to use GNSS when available for high accuracy while falling back to INS independently when GNSS signals are blocked or delayed, resolving the contradiction between accuracy and reliability
Solution Approach 2:
The system dynamically changes the weighting parameters in the Kalman filter based on GNSS signal quality indicators. When signal blockage or delays are detected, the filter automatically adjusts parameters to rely more on INS data, maintaining reliability while preserving accuracy through adaptive parameter adjustment
2Duration of action of stationary object
If high-rate sensor data is buffered for real-time processing, then navigation continuity is improved, but device complexity increases due to additional buffering and prediction mechanisms
Solution Approach 1:
The system performs preliminary buffering of high-rate INS sensor data at predictable time intervals, and pre-computes prediction values using the Kalman filter before GNSS updates arrive. This preliminary action ensures navigation continuity without requiring complex real-time synchronization mechanisms when GNSS data arrives with delays
Solution Approach 2:
The Kalman filter acts as an intermediary that reconciles the timing mismatch between continuous high-rate INS data and delayed GNSS updates. It uses prediction phases to estimate states at intermediate times and correction phases to update with actual GNSS measurements, maintaining continuity without adding significant computational complexity
3Measurement precision
If GNSS measurement updates are delayed, then real-time navigation accuracy deteriorates, but processing time is reduced by handling updates asynchronously
Solution Approach 1:
The system implements dynamic time-stamping and asynchronous update mechanisms where the Kalman filter adapts its prediction horizon based on actual GNSS update timing. This allows the system to maintain navigation accuracy by dynamically adjusting to variable update intervals rather than requiring fixed real-time synchronization, reducing the impact of delays
Data Source
AI summary
A navigation module for providing a real-time INS/GNSS navigation solution for a moving object comprising a receiver for receiving absolute navigational information from an external source and an assembly of self-container sensors for generating navigational information. The module also contains a processor coupled to receive the output information from the receiver and sensor assembly, and integrate the output information in real-time to produce an overall navigation solution. The overall navigation solution will contain a main navigation solution task, and at least one other task, where the other task is used to enhance the overall navigation solution.