Kalman Filter Navigation State Estimation for Multi-Path Error Mitigation
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Solution Overview
Problem
The Kalman filter used in navigation systems for mobile carrier devices is prone to errors due to the multi-path phenomenon, which affects the accuracy of navigation state estimation when using GPS signals reflected by satellites.
Innovation Solution
A method that involves obtaining delta range and kinematic data from positioning signals, performing tests on delta range innovations to determine if the signal has followed a multi-path, and conditionally using this data to update the navigation state, ensuring robustness against multi-path errors by implementing a tight inertial/satellite hybridization and using pseudo-distances as observations only if the signal is deemed path-free.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS positioning signals are used for navigation updates, then navigation accuracy is improved, but multi-path errors reduce reliability
Solution Approach 1:
The patent applies preliminary action by performing innovation tests on GPS signals before using them for navigation updates. The system calculates innovations (differences between measured and predicted values) and tests these innovations against thresholds to detect multi-path errors in advance, preventing corrupted data from degrading navigation accuracy
Solution Approach 2:
The patent implements feedback through continuous monitoring of signal innovations and adaptive selection of update sources. The system constantly compares measured GPS values with predicted values, uses the innovation tests to provide feedback on signal quality, and dynamically switches between GPS updates and alternative sources based on this feedback
2Productivity
If all positioning signals are used for updates, then data utilization is improved, but error propagation increases
Solution Approach 1:
The patent applies local quality by treating different GPS signals differently based on their individual quality assessments. Instead of uniformly accepting or rejecting all signals, the system performs separate innovation tests on each signal and selectively uses only those that pass the quality thresholds, applying different acceptance criteria to different data sources
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the selection criteria for signal acceptance based on observed innovation magnitudes. The system modifies which signals are used for updates by changing the effective parameter of signal inclusion/exclusion based on real-time quality assessment
Data Source
AI summary
A method for navigating a carrier using a Kalman filter estimating a navigation state of a carrier, comprising: obtaining, from a signal transmitted by the satellite and subsequently received by the carrier, a delta range measured between the carrier and a satellite and another measured kinematic datum which is associated with the satellite, generating, from position data for the carrier and the satellite in the navigation state, an estimated delta range between the carrier and the satellite, calculating, using the delta ranges, a delta range innovation associated with the satellite, carrying out a test on the delta range innovation, the test result indicating whether or not the signal was a multi-path signal, using, by means of the filter, the kinematic datum as an observation to update the navigation state provided that the test result indicates that the signal was not a multi-path signal.