Road GNSS Geolocation Quality Prediction Using DOP and Urban Multipath Data
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Solution Overview
Problem
Existing methods for predicting GNSS geolocation quality in urban environments fail to adequately consider the impairment of GNSS accuracy due to multi-path effects caused by buildings, which significantly compromise positioning accuracy.
Innovation Solution
A method for predicting GNSS geolocation quality that incorporates both GNSS signal availability and multi-path reception by using a model that considers DOP values and characteristic environmental parameters, such as building dimensions and arrangements, to improve accuracy in urban settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only DOP values and line-of-sight conditions are considered for predicting GNSS geolocation quality, then the prediction method remains simple, but the accuracy of geolocation quality prediction deteriorates in urban environments due to unaccounted multi-path effects
Solution Approach 1:
The prediction model is segmented into two independent components: one evaluating line-of-sight conditions (DOP values) and another evaluating multi-path effects (environmental parameters like building dimensions and arrangements). This segmentation allows each component to be optimized independently while combining to provide comprehensive geolocation quality prediction.
Solution Approach 2:
The patent transitions from a single-dimension prediction approach (only DOP values) to a multi-dimensional approach by adding environmental parameters as a new dimension. This includes building dimensions, building arrangements, and other local environmental factors that independently influence multi-path effects, thereby enhancing prediction accuracy without excessive complexity.
2Reliability
If environmental parameters describing building dimensions and arrangements are incorporated into the prediction model, then multi-path effects are adequately considered, but the complexity of the prediction system increases
Solution Approach 1:
The model structure is segmented into modular components where environmental parameters are processed separately from DOP values. Each parameter type (building dimensions, building arrangements) can be evaluated independently and then combined, improving reliability while managing complexity through structured organization.
Solution Approach 2:
The prediction model is designed with universal applicability to various urban environments by using general environmental parameters that can describe different building configurations. The same model structure can handle diverse scenarios (different city layouts, building densities, and configurations) without requiring environment-specific customization, thereby improving reliability across applications while avoiding excessive complexity.
Data Source
AI summary
A method for predicting GNSS geolocation quality on roads in urban environments given the propagation of objects obstructing GNSS signals is disclosed. The method includes step a) providing a model for determining the at least one geolocation quality parameter as a function of input values, wherein the model is designed for at least the following input values: (i) at least one DOP value describing the quality of the present geometrical satellite constellation under line-of-sight conditions, and (ii) at least one characteristic environmental parameter considered in the model independently of the DOP value and describing a local influence of objects on the propagation of GNSS signals. The method further includes step b) determining a location and/or a time at which location and/or time the GNSS geolocation quality is to be predicted. In addition, the method includes step c) determining at least one DOP value at a given time as a function of the location of the GNSS receiver provisionally determined in step b) and providing the DOP value as an input value to the model. The method also includes step d) determining at least one characteristic environmental parameter as a function of the location provisionally determined in step b) and providing the at least one environmental parameter as an input value to the model. Also, the method includes step e) calculating at least one quality parameter describing the quality of a GNSS geolocation using the model provided in step a) as a function of the parameters provided in steps c) and d).