Doppler Shift Correction Using 3D Building Models
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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 reflected with minimal loss but distorted.
Innovation Solution
The use of 3D building models to determine a lower bound of uncertainty for estimated positions and provide Doppler corrections, which account for signal reflections and improve the accuracy of position and velocity estimates by distinguishing between clock drift and mis-estimation of relative motion.
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 system performs preliminary identification of specular reflectors using 3D building models before processing GNSS signals. By pre-determining the locations and characteristics of reflective surfaces in the urban environment, the system can anticipate and correct for signal path distortions before they affect positioning accuracy
Solution Approach 2:
The patent introduces an intermediary processing layer that separates direct line-of-sight signals from reflected signals. This intermediary mechanism uses 3D building models to identify signal paths and applies selective processing to correct Doppler shifts in reflected signals while maintaining accuracy from direct signals
2Device complexity
If C/N0 strength is used as a metric of PVT solution fidelity, then processing is simplified, but positioning uncertainty becomes biased incorrectly lower
Solution Approach 1:
The system changes the evaluation parameter from simple C/N0 strength to a composite metric that incorporates 3D building model data and signal path analysis. This parameter transformation allows the system to maintain processing efficiency while achieving more accurate assessment of positioning uncertainty by considering geometric relationships between satellites, reflectors, and the receiver
3Device complexity
If Doppler correction is applied without distinguishing reflection types, then processing is simpler, but positioning error increases due to incorrect correction
Solution Approach 1:
The Doppler correction process is segmented into distinct stages: identification of signal path type (direct vs. reflected), calculation of appropriate correction factors for each path type, and selective application of corrections. This segmentation allows the system to apply simplified corrections to direct signals while applying more complex reflection-aware corrections only where needed
Solution Approach 2:
Instead of trying to identify direct signals and subtract reflections, the system inverts the approach by identifying reflected signals through 3D building model analysis and correcting only those specific paths. This inversion simplifies processing by focusing computational resources on the problematic reflected signals rather than attempting to filter them from all signals
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 and provides more accurate position and velocity estimates by accounting for signal reflections, thereby decreasing uncertainty and avoiding incorrect position solutions in challenging GNSS environments.
Implementation Method 1
The processor can determine a probable path for a signal from a GNSS space vehicle (e.g., a satellite) to reach the GNSS receiver. The probable path can include one or more specular reflections.
Implementation Method 2
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 can determine a probable path for a signal from a GNSS space vehicle (e.g., a satellite) to reach the GNSS receiver. The probable path can include one or more specular reflections. 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. The processor can then incorporate the Doppler correction in an estimated velocity of the mobile device, an estimated position of the mobile device, or both.