Vehicle Location With UWB Delay Compensation and Kalman Filtering
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Solution Overview
Problem
Existing vehicle location methods using inertial measurement units and ultra-wide band transceivers suffer from inaccuracies due to measurement delays and movement during radio signal exchanges, leading to errors in determining vehicle position, orientation, and velocity.
Innovation Solution
A method that incorporates an inertial navigation system with a closed-loop integration scheme, utilizing an Error State Kalman Filter to correct vehicle position, orientation, and velocity by combining inertial measurements with ultra-wide band distance measurements, accounting for vehicle movement and measurement delays through a corrective term based on apparent velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultra-wide band transceivers are used to measure distance between fixed and mobile transceivers, then location accuracy is improved, but measurement delays and movement during signal exchange introduce errors that worsen precision
Solution Approach 1:
The patent applies preliminary action by recording the transmission and reception instants of radio signals during the distance measurement process. These timestamped measurements are captured in advance and stored for subsequent processing, allowing the system to account for measurement delays by referencing the actual transmission and reception times rather than assuming simultaneous measurement.
Solution Approach 2:
The patent implements feedback by using an Error State Kalman Filter that continuously processes the timestamped distance measurements and compares them with position estimates from the inertial navigation system. The filter provides corrective feedback to adjust the estimated position, orientation, and velocity, compensating for errors introduced by measurement delays and vehicle movement during signal exchange.
2Measurement precision
If inertial measurement unit data is integrated with radio signal distance measurements, then vehicle position accuracy is improved, but vehicle movement during measurement introduces errors
Solution Approach 1:
The Error State Kalman Filter provides continuous feedback by comparing inertial navigation estimates with ultra-wide band distance measurements. The filter processes the difference between measured and estimated positions to generate corrective terms that adjust the vehicle position, orientation, and velocity estimates, compensating for movement-related errors.
Solution Approach 2:
The patent introduces an intermediary processing layer in the form of the Error State Kalman Filter that mediates between the inertial measurement unit and the ultra-wide band transceiver data. This intermediary fuses the two data sources, reconciling their different characteristics and compensating for the effects of vehicle movement during measurement.
3Measurement precision
If closed-loop integration with Error State Kalman Filter is implemented, then location accuracy is significantly improved, but system complexity increases
Solution Approach 1:
The patent merges multiple subsystems (inertial navigation system, ultra-wide band transceivers, and Error State Kalman Filter) into an integrated closed-loop system. By combining these elements, the patent achieves improved location accuracy through data fusion and error compensation, while the unified architecture manages complexity through systematic integration rather than separate independent systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of vehicle location by compensating for measurement delays and movement-related errors, providing precise position, orientation, and velocity data.
Implementation Method 1
In a known manner, the system 30 uses measurements from a satellite geolocation unit 40 (GNSS) and from an inertial measurement unit 42 to determine the position, the orientation and the velocity of the vehicle 2
Implementation Method 2
a distance between the fixed and mobile transceivers measured from the transmission or reception instants of radio signals exchanged between the fixed and mobile transceivers
Implementation Method 3
A method is proposed that uses an inertial navigation system with a closed-loop integration scheme, utilizing an Error State Kalman Filter to correct vehicle position, orientation, and velocity
Data Source
AI summary
A method includes: a) triggering, at an instant tuj, an exchange of radio signals between a mobile transceiver fixed to the vehicle and a fixed transceiver; then b) computing a rough distance only from the transmission or reception instants of the exchanged radio signals; (c) constructing an estimated position of the vehicle at an instant tm; (d) correcting the computed rough distance, then using this corrected rough distance to correct the estimated position. This correction comprises: computing an apparent velocity movement va0 of the vehicle towards the fixed transceiver; then adding a corrective term δd0 to the computed rough distance, which corrective term is computed from the following relation: δd0=va0·DT0, where DT0 is equal to the elapsed time between the instants tuj and tm.