Navigation System Stationary to In-Motion Alignment Transition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional inertial navigation systems face significant time consumption and accuracy issues when a vehicle moves before alignment is completed in stationary alignment mode, as the alignment process is interrupted and requires restarting, leading to corrupted estimates during the delay between actual motion and detection.
Innovation Solution
The implementation of a stationary alignment Kalman filter (SAKF) for generating state estimates and a continuous alignment filter (CAF) that provides an uncorrupted secondary solution, which is used by an in-motion alignment filter to complete alignment efficiently and accurately, accounting for uncertainty and corrections during the delay period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses conventional stationary alignment mode, then alignment accuracy is achieved when stationary, but alignment time increases extensively when motion occurs before alignment completion
Solution Approach 1:
The system dynamically transitions from stationary alignment mode to in-motion alignment mode based on real-time motion detection. The alignment filter adapts its operation mode to match the current operational state, allowing the system to maintain accuracy while reducing time loss when motion occurs during alignment.
Solution Approach 2:
The system changes the operational parameters of the alignment filter by switching between stationary and in-motion alignment modes. This parameter change allows the filter to process data appropriately for the current state, preventing time loss without compromising the accuracy that would be achieved through proper stationary alignment when available.
2Measurement precision
If the system restarts stationary alignment after motion detection, then alignment accuracy can be maintained, but productivity decreases due to extensive time consumption
Solution Approach 1:
The system maintains continuous alignment processing by transitioning to in-motion alignment mode rather than restarting stationary alignment. This continuous action preserves alignment progress and maintains quality while significantly improving productivity by avoiding repeated restarts and extensive time consumption.
Solution Approach 2:
The system performs preliminary stationary alignment when the vehicle is stationary, establishing an initial accurate baseline. When motion is detected, it seamlessly transitions to in-motion mode rather than restarting, thereby maintaining the benefits of preliminary accurate alignment while improving overall efficiency.
3Loss of time
If the system detects motion with delay, then stationary alignment can continue briefly, but measurement precision deteriorates due to corrupted estimates
Solution Approach 1:
The system dynamically switches from stationary to in-motion alignment mode upon motion detection, allowing it to tolerate detection delays without corrupting estimates. The dynamic mode change ensures that data processing remains appropriate for the current state, preserving measurement precision even when motion is not detected instantaneously.
Solution Approach 2:
The in-motion alignment mode serves as an intermediary state that bridges the gap between stationary alignment and full operational mode. This intermediary mode allows the system to handle the transition period caused by detection delays without corrupting estimates, maintaining measurement precision throughout the transition.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A navigation system to transition from a stationary alignment filter to an in-motion alignment filter is provided. The system comprises a processing unit configured to implement a stationary alignment Kalman filter (SAKF) in gyrocompass alignment mode to generate state estimates and provide corrections when the object is stationary, and to implement an algorithm to compute a covariance for the SAKF that accounts for uncertainty in the SAKF estimates; wherein the processing unit is further configured to implement a continuous alignment filter (CAF) that generates a secondary solution which remains unaffected by the SAKF corrections during a delay period accommodating a delay between the time of actual motion to the time of detected motion, and to implement an algorithm to compute a covariance for CAF that accounts for the uncertainty in CAF during delay period; and wherein outputs of the CAF and its covariance are communicated to an in-motion alignment filter.