Positioning System Targeted Data Collection for Accuracy
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Solution Overview
Problem
Existing positioning systems face challenges in selectively collecting high-quality crowd-sourced data, leading to degraded user experience due to battery life, processing time, and storage issues, as they struggle to effectively exclude poor quality data and adjust data collection frequency based on device performance and location.
Innovation Solution
A positioning system that selectively targets devices for data collection based on performance targets, prioritizing devices that provide high-quality data and adjusting data collection in underperforming areas to meet predefined performance metrics, such as positional accuracy and response time, while minimizing data collection in overperforming areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If crowd-sourced data collection is increased to improve positioning system performance, then data quality and system accuracy improve, but user experience degrades due to battery life, processing time, and storage consumption
Solution Approach 1:
The system dynamically changes data collection parameters (frequency, quantity, device selection) based on current positioning accuracy metrics and performance targets, adjusting these parameters to optimize the balance between accuracy improvement and energy consumption
Solution Approach 2:
The system continuously monitors positioning performance and compares it against targets, using this feedback to adjust data collection strategies, reducing collection when targets are met and increasing collection when performance gaps exist
2Measurement precision
If data collection frequency is increased to improve positioning system performance, then system accuracy improves, but processing time and storage requirements increase
Solution Approach 1:
The system collects data from only the necessary subset of devices required to meet performance targets, rather than collecting data from all available devices, thus achieving sufficient accuracy with reduced processing time
Solution Approach 2:
The system adjusts data collection parameters dynamically, changing the frequency and quantity of data collection based on current performance gaps, reducing processing time when high accuracy is already achieved
3Measurement precision
If existing systems limit crowd sourcing to certain device types and operating systems, then data quality control improves, but the ability to adjust performance and minimize impact on devices remains limited
Solution Approach 1:
The system transitions from static device filtering to dynamic performance-based adjustment, where data collection parameters are continuously adapted based on real-time performance monitoring and target comparison across all device types
Solution Approach 2:
The system applies a universal performance target framework that works across all device types and operating systems, replacing device-specific filtering with a unified approach that achieves both quality control and performance adaptability
Data Source
AI summary
Selecting devices from which to receive data for adjusting the performance of a positioning system. The positioning system infers the location of the devices based on beacons observed by the devices. The performance of the positioning system is compared to performance targets. One or more of the devices are selected based on the comparison. Data collection from the devices is adjusted to affect performance of the positioning system (e.g., improved or reduced). For example, if the positioning system predicts positions poorly for a particular area, data collection from selected devices within the particular area may be increased.