Positioning System Using Weighted Multi-Source Estimates
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Solution Overview
Problem
Existing positioning systems often rely on a single network for estimating a mobile device's position, which limits accuracy and certainty, especially in varying environments.
Innovation Solution
Combining position estimates and uncertainty metrics from multiple positioning technologies, such as satellite, terrestrial, and local beacon networks, to improve accuracy and certainty by correcting pseudoranges, adjusting weights, and using mapping functions to refine uncertainty metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single positioning network is used to estimate mobile device position, then the system complexity is low, but the position accuracy and certainty are limited
Solution Approach 1:
The patent combines position estimates and uncertainty metrics from multiple positioning networks (satellite, terrestrial, local beacon networks) into a unified position estimate. This merging of multiple data sources improves measurement precision by leveraging the strengths of different networks while compensating for their individual limitations.
Solution Approach 2:
The system is designed to work with multiple types of positioning networks simultaneously, making it universally applicable across different environments. The multi-functionality allows the system to adapt to varying conditions by selecting and combining appropriate positioning sources based on availability and reliability.
2Measurement precision
If multiple positioning networks are combined to improve position accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system performs self-service by automatically evaluating uncertainty metrics from different positioning networks and autonomously determining the optimal combination of position estimates. This self-service approach manages the increased complexity internally without requiring external intervention, allowing the system to handle multiple networks efficiently.
Solution Approach 2:
The system changes parameters by adjusting the weighting of different positioning networks based on their uncertainty metrics. By dynamically modifying these parameters according to environmental conditions and network performance, the system optimizes position accuracy while managing complexity through adaptive parameter adjustment.
3Reliability
If position estimates from multiple networks are integrated, then the reliability of position estimates improves, but the processing complexity increases
Solution Approach 1:
The system uses uncertainty metrics as feedback to continuously evaluate and adjust the combination of position estimates from different networks. This feedback mechanism improves reliability by systematically incorporating information about the quality and certainty of each position estimate, allowing the system to adapt to changing conditions and maintain high reliability.
Data Source
AI summary
Estimating a position of a mobile device. Particular systems and methods for estimating a position of a mobile device using information from two positioning technologies determine different position estimates for the mobile device using different positioning technologies, and determine a final position estimate for the mobile device using a weighted combination of the different position estimates. In some implementations, the weighted combination is a weighted average or a weighted median of the different position estimates. Weights may be determined using respective uncertainty metrics corresponding to the respective position estimates.