Indoor Positioning Accuracy Diagnosis via Machine Learning Feedback
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Solution Overview
Problem
Indoor positioning systems face challenges in maintaining accurate location determination due to factors like varying floor plans, AP-AP distances, and interference, making it difficult to diagnose and rectify accuracy issues effectively.
Innovation Solution
A device uses a machine learning model to identify contributing factors affecting location accuracy in indoor positioning systems by analyzing characteristic data and initiates remediation actions based on these findings, employing a learning pipeline architecture that aggregates data from multiple deployments to build a knowledge base for common accuracy issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional indoor positioning systems are deployed, then location determination can be achieved, but location accuracy deteriorates due to varying floor plans, AP-AP distances, and interference
Solution Approach 1:
The system collects characteristic data from multiple indoor positioning deployments and uses machine learning models to analyze patterns and provide feedback for optimizing positioning accuracy. The feedback mechanism identifies contributing factors to accuracy degradation and enables remediation actions to counteract harmful environmental factors.
Solution Approach 2:
The system changes parameters by aggregating characteristic data from multiple deployments and using machine learning to identify optimal configurations. It analyzes variations in AP-AP distances, floor plan characteristics, and interference patterns to determine parameter adjustments that improve location accuracy in different environmental conditions.
2Difficulty of detecting and measuring
If more characteristic data is collected to diagnose accuracy issues, then diagnostic capability improves, but system complexity increases
Solution Approach 1:
The system uses a universal machine learning model that can process multiple types of characteristic data (deployment information, environment information, accuracy measurements) from various sources. This multi-functional approach enables the same diagnostic framework to handle different data types and deployment scenarios without requiring separate complex analysis systems for each.
Solution Approach 2:
The machine learning model acts as an intermediary between raw characteristic data and diagnostic insights. It mediates the complex data collection process by automatically identifying patterns and contributing factors, reducing the burden on users to manually analyze complex datasets while maintaining high diagnostic capability.
3Measurement precision
If machine learning models are used to identify contributing factors, then accuracy issue diagnosis improves, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and aggregating characteristic data from multiple deployments before applying machine learning analysis. It prepares deployment information, environment information, and accuracy measurements in advance, organizing them into structured formats that reduce the computational burden during actual diagnostic operations.
Solution Approach 2:
The system applies partial action by focusing machine learning analysis on specific contributing factors and characteristics most relevant to accuracy degradation. Rather than analyzing all possible data equally, it identifies and concentrates computational resources on the most impactful factors, achieving effective diagnosis with reduced computational requirements.
Data Source
AI summary
In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.