GNSS Ray Tracing for Urban Non-Line-of-Sight Positioning
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Solution Overview
Problem
GNSS systems struggle with accurate location determination in environments where line-of-sight paths are obstructed, such as dense urban areas, leading to inaccurate distance estimations due to signal reflections.
Innovation Solution
Implementing ray tracing techniques and building models to simulate non-line-of-sight signal paths, allowing the mobile device to project rays at different angles and locations to determine the most likely path between the device and satellites, using measured signal flight times for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If line-of-sight GNSS methods are used, then location determination is simple and fast, but accuracy deteriorates in obstructed environments such as dense urban areas
Solution Approach 1:
The system performs preliminary ray tracing simulations to generate expected signal paths and delays before actual GNSS signal reception. By pre-computing the environmental impact on signal propagation using building models and ray tracing algorithms, the system prepares reference data that guides subsequent signal measurement and matching processes, enabling accurate location determination in obstructed environments without real-time computational burden
Solution Approach 2:
The system introduces ray tracing simulations and building models as intermediary components between the GNSS receiver and the location calculation process. These intermediaries model the environmental effects on signal propagation, creating virtual signal paths that mediate between raw satellite signals and final position estimates, thereby improving accuracy in non-line-of-sight conditions
2Measurement precision
If ray tracing techniques are implemented to simulate non-line-of-sight paths, then location accuracy in obstructed environments improves, but computational complexity and processing time increase
Solution Approach 1:
Ray tracing simulations are performed in advance to generate expected signal paths, reflection points, and time delays for various potential receiver locations. This pre-computation creates a lookup structure that accelerates real-time signal matching, reducing processing time during actual location determination while maintaining high accuracy
Solution Approach 2:
The system computes ray tracing results for a limited set of candidate locations or uses simplified building models with key reflective surfaces only, rather than performing exhaustive simulations for all possible paths. This partial computation approach provides sufficient accuracy for practical applications while significantly reducing computational burden and processing time
3Measurement precision
If multiple signal paths including reflections are considered, then measurement accuracy improves, but the complexity of identifying and processing each path increases
Solution Approach 1:
The system uses feedback from ray tracing simulations to guide signal path identification and measurement processes. Expected reflection points, path delays, and signal strengths from simulations serve as feedback criteria to validate and select actual measured paths, reducing the complexity of identifying correct paths among multiple possibilities and improving measurement accuracy
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 location accuracy by considering non-line-of-sight paths, reducing errors caused by signal reflections and improving geometrical strength of position solutions.
Implementation Method 1
The non-line-of-sight signal paths can be determined by comparing the simulated signal's flight time against the measured flight times for the received signals
Data Source
AI summary
Techniques may include receiving signal measurements of one ranging signal that is transmitted by a first satellite of a global navigation satellite system, where the one ranging signal is measured by the device's antennas, each measurement corresponding to a different path to the antennas of the mobile device. In addition, the techniques may include identifying a path delay for each signal measurement. Techniques may include simulating, for different locations, possible paths between the mobile device and the first satellite, where the possible paths include at least one path that reflects from a building in a map model around a previously measured location of the mobile device; determining an estimated path delay for each simulated path; comparing the path delay for each signal measurement to each estimated path delay to identify at least one matching simulated path for each signal measurement; and determining a location of the mobile device.