GNSS Vehicle Localization Correction for Urban Multipath Errors
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Solution Overview
Problem
In urban environments, especially in narrow street canyons, GNSS reception is impaired due to multipath effects, particularly in the NLOS case, leading to significant positioning errors.
Innovation Solution
A method for GNSS-based vehicle localization that involves receiving GNSS satellite signals, determining environmental information using image data from vehicle sensors, calculating correction information based on this data, and applying this correction to the distance information to improve positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If GNSS signals are received in urban environments with buildings, then positioning coverage is improved, but positioning accuracy deteriorates due to multipath effects and NLOS signals
Solution Approach 1:
The patent introduces environment sensors (cameras, LIDAR, radar) as intermediary devices that capture images of the surrounding environment. These sensors act as mediators between the GNSS receiver and the multipath effects, providing environmental data that helps identify and correct for reflected signals. The environment information serves as an intermediary layer that enables the system to distinguish between direct and reflected signals without requiring changes to the GNSS satellites themselves.
Solution Approach 2:
The patent implements a feedback mechanism where environment sensor data is continuously fed back to the signal processing unit. The processor uses this environmental information to identify multipath conditions and adjust the pseudo-range measurements accordingly. This closed-loop feedback allows the system to adapt to changing urban environments and maintain positioning accuracy despite the presence of buildings and reflections.
2Reliability
If reflected GNSS signals are received, then signal availability is improved, but distance measurement accuracy deteriorates due to overestimated pseudo-range
Solution Approach 1:
The patent converts the harmful effect of reflected signals into a beneficial one by using environment sensors to detect buildings and surfaces that cause reflections. The same environmental features that create multipath errors are detected by cameras and LIDAR, and this information is then used to calculate correction values. The system transforms the problematic reflected signals into useful environmental data that enables correction of the pseudo-range measurements.
Solution Approach 2:
The patent changes the parameter being measured from raw pseudo-range to corrected distance. By introducing correction values based on environment sensor data, the system transforms the inaccurate pseudo-range measurements into accurate distance measurements. The processor dynamically adjusts the distance parameter by subtracting calculated correction values that account for the additional path length introduced by signal reflections.
3Measurement precision
If environment sensors are added to capture images for correction, then positioning accuracy in NLOS conditions is improved, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by using environment sensors that serve multiple purposes. The same cameras, LIDAR, or radar units that capture environmental images for multipath correction are also used for autonomous driving functions such as obstacle detection, navigation, and environmental mapping. This universal use of sensors reduces the need for dedicated hardware and justifies the added complexity through multiple beneficial applications.
Solution Approach 2:
The patent merges the multipath correction function with the autonomous driving system. Rather than adding separate dedicated sensors solely for GNSS correction, the system combines environmental sensing for both autonomous navigation and positioning correction into a unified architecture. The processor integrates both functions, using the same environmental data for both obstacle avoidance and pseudo-range correction, thereby reducing overall system complexity.
Data Source
AI summary
The disclosure relates to a method for GNSS-based localization of a vehicle, comprising at least the following steps: a) receiving GNSS-satellite signals from GNSS satellites and determining at least one item of distance information about the distance between the vehicle and the GNSS satellite emitting the relevant GNSS-satellite signal, b) determining at least one item of environmental information about the environment around the vehicle using image information determined using at least one environment sensor of the vehicle, which is capable of capturing images of at least part of the environment around the vehicle from different perspectives, c) determining at least one item of correction information using the at least one environmental information item, and d) correcting the at least one distance information item by means of the at least one correction information item.