GNSS Positioning Using 3D Building Models to Resolve Urban Canyon Reflections
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Solution Overview
Problem
Conventional GNSS technologies fail to accurately determine position and velocity in challenging environments like urban canyons due to specular reflections, which cause positioning errors and over-confidence in location estimation, especially when signals are distorted but nearly lossless, leading to incorrect error margins.
Innovation Solution
The use of 3D building models to determine a lower bound of uncertainty for GNSS positioning, incorporating Doppler corrections to differentiate between clock drift and mis-estimation of relative motion, and providing these corrections to the GNSS receiver to improve position and velocity estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GNSS signal processing is used, then positioning can be obtained, but positioning accuracy deteriorates in urban canyon environments due to specular reflections
Solution Approach 1:
The patent introduces an intermediary processing layer between signal reception and position determination. This layer analyzes signal characteristics (C/N0, signal structure) and uses 3D building models to identify specular reflections, acting as a mediator that filters out harmful reflected signals before they corrupt the position solution.
Solution Approach 2:
The system performs preliminary analysis of signal characteristics and environmental context (using 3D building models) before final position determination. By pre-identifying potential specular reflections and adjusting uncertainty values in advance, the system prevents corrupted signals from misleading the position solution.
2Device complexity
If C/N0 strength is used as a metric of fidelity, then processing is simplified, but positioning uncertainty becomes biased incorrectly lower due to over-confidence in specularly reflected signals
Solution Approach 1:
The system implements feedback by continuously monitoring signal characteristics (C/N0, signal structure) and comparing them against expected patterns. When specular reflections are detected through this feedback mechanism, the system adjusts the uncertainty values accordingly, preventing over-confidence in corrupted measurements.
Solution Approach 2:
The patent dynamically changes the uncertainty parameter based on detected signal conditions. When specular reflections are identified through signal structure analysis and 3D building model comparison, the system increases the uncertainty value to reflect the reduced reliability of the measurement, even if C/N0 remains high.
3Measurement precision
If 3D building models and Doppler corrections are incorporated, then positioning accuracy improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-acquiring 3D building models of the environment and pre-calculating expected signal paths including specular reflections. This preparation work is done before GNSS signal processing, so that during actual positioning, the system can quickly compare received signals against pre-computed expectations without excessive real-time computation.
Solution Approach 2:
The patent creates a virtual copy of the physical environment through 3D building models. This digital twin allows the system to simulate and analyze signal propagation paths, identify potential specular reflections, and determine appropriate uncertainty adjustments without requiring complex real-time physics calculations during actual positioning.
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
This approach reduces positioning errors, avoids over-confident location estimation, and provides more accurate position and velocity estimates by accounting for specular reflections, thus decreasing uncertainty and correcting for incorrect position solutions.
Implementation Method 1
The processor can determine a Doppler correction based on the probable path, including inverting a sense of a vector of the Doppler correction for each reflection.
Data Source
AI summary
Techniques for GNSS positioning using three-dimensional (3D) building models are described. A processor of a mobile device can determine a lower bound of uncertainty for an estimated position of the mobile device. The processor can receive an estimated position from a GNSS receiver of the mobile device. The processor can acquire geographic feature data including 3D building models of buildings and other geographic features that are located near the estimated position and may reflect GNSS signals. The processor can then determine a lower bound of uncertainty of the estimated position, regardless of an estimated uncertainty provided by a GNSS estimator. The lower bound can be higher (e.g., have a greater error margin) than the uncertainty value provided by the GNSS estimator. The processor can then present the estimated position, in association with an error margin corresponding to the lower bound of uncertainty, on a map user interface of the mobile device.