GPS Error Correction Using Satellite Data for Urban Positioning
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Solution Overview
Problem
Pedestrian GPS devices suffer from low accuracy in urban areas due to multiple path reflections, which existing technologies have not effectively addressed, leading to navigation challenges and missed encounters between drivers and passengers.
Innovation Solution
A two-part method using deep learning to create an error correction function based on GPS coordinates and satellite data, collecting multiple time samples from vehicles to derive a correction function, and applying it to pedestrian devices to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signals are received in urban areas with high buildings, then satellite coverage is available, but positioning accuracy deteriorates due to multiple path reflections
Solution Approach 1:
The patent introduces an intermediary correction system that mediates between the GPS receiver and the true position. Multiple reference receivers collect positioning data, and a correction server processes this data to generate correction values that compensate for urban environment errors. This intermediary layer filters out the harmful effects of multiple path reflections before the final position is determined.
Solution Approach 2:
The system implements feedback by continuously collecting positioning data from multiple reference receivers at known locations, comparing these measurements with true positions, and using the discrepancies to generate real-time correction values. This feedback loop allows the system to adapt to changing urban environment conditions and continuously improve positioning accuracy.
2Measurement precision
If conventional GPS correction methods are applied, then some accuracy improvement is achieved, but the solution is not cost-effective for widespread pedestrian use
Solution Approach 1:
The patent creates a universal correction system that serves multiple purposes and users. The same infrastructure of reference receivers and correction server benefits all pedestrians in the coverage area simultaneously. This multi-functionality amortizes the costs across many users, making the solution cost-effective for widespread deployment compared to individual expensive correction systems.
Solution Approach 2:
The system enables self-service by allowing ordinary smartphones with standard GPS receivers to achieve improved accuracy without requiring specialized expensive hardware. Users simply need to install the application that receives correction data, and the system automatically applies corrections to their position calculations, making high-precision GPS accessible to mass market consumers.
3Measurement precision
If multiple reference receivers are deployed to improve accuracy, then positioning precision increases, but system complexity increases
Solution Approach 1:
The patent extracts the complex computation and coordination functions from individual receiver units and concentrates them in a centralized correction server. Each reference receiver only needs to collect and transmit raw positioning data, while the complex tasks of data fusion, error analysis, and correction value generation are performed centrally. This extraction simplifies the individual receiver devices while maintaining high overall system accuracy.
Solution Approach 2:
The system merges the functionality of multiple reference receivers and their data processing into a unified correction service. Instead of each receiver operating independently with full processing capability, they are combined into a coordinated network where data from multiple receivers is merged and processed together to generate a single set of correction values that benefit all users in the area.
Data Source
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AI summary
A method for creating a correction function for improving the accuracy of a GPS device collects multiple time samples at multiple known locations wherein each time sample consists of GPS coordinates and associated satellite data from multiple satellites. The satellite data includes or permits determination of (i) satellite azimuth and elevation of an associated satellite, (ii) Signal-to-Noise Ratio of a received signal from the associated satellite, and optionally (iii) pseudo-range. For each time sample a respective error between the known location and the corresponding GPS coordinates is computed and an error correction function is created as a function of the respective GPS coordinates and the satellite data by applying deep learning/machine learning techniques to the multiple time samples.