Criterion-Based Indoor Localization for Mobile Devices
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Solution Overview
Problem
Conventional indoor navigation and localization techniques face challenges in accurately localizing mobile devices in indoor spaces due to varying environmental conditions and sensor behavior, leading to unreliable positioning and high computational costs.
Innovation Solution
The proposed solution involves criterion-based calibration of indoor spaces, where sensorial data is analyzed to determine behavioral patterns and select the most reliable data types for localization. This approach segregates indoor spaces into zones based on influencing factors and assigns a localization criterion for each zone, optimizing the use of sensorial data for accurate and efficient localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based navigation systems are used for indoor localization, then outdoor positioning accuracy is improved, but indoor positioning reliability deteriorates due to line-of-sight requirements and signal unavailability in enclosed spaces
Solution Approach 1:
The patent divides the indoor space into multiple zones based on environmental characteristics (e.g., open areas, corridors, rooms with different signal propagation properties). Each zone is assigned specific localization criteria and sensor configurations, allowing the system to adapt to local conditions rather than using a uniform approach throughout the entire indoor environment.
Solution Approach 2:
The patent applies different localization strategies and sensor combinations tailored to specific zone characteristics. For example, Wi-Fi fingerprinting may be prioritized in zones with strong wireless signals, while inertial sensors are weighted more heavily in zones with poor signal propagation. This localized adaptation optimizes positioning performance for each specific environmental context.
2Reliability
If multiple sensor types are used for indoor localization, then positioning reliability is improved, but computational cost and device complexity increase
Solution Approach 1:
The patent implements dynamic sensor selection and weighting that adjusts in real-time based on current environmental conditions and zone characteristics. The system continuously evaluates sensor data quality and reliability, dynamically adjusting which sensors are active and how their data is weighted in the fusion algorithm, rather than using a fixed sensor configuration.
Solution Approach 2:
The patent changes operational parameters such as sensor sampling rates, fusion algorithm weights, and data processing intensity based on the current zone and positioning requirements. For example, in zones with high signal stability, the system may reduce sampling frequency to lower computational load, while in zones with rapidly changing conditions, higher sampling rates are employed.
3Measurement precision
If comprehensive sensor data processing is performed for accurate localization, then positioning accuracy is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent processes only the necessary subset of sensor data required for acceptable positioning accuracy in each specific zone, rather than uniformly processing all available sensor data throughout the entire system. The system identifies and processes only the most relevant sensors and data types for each zone's characteristics, reducing unnecessary computational overhead.
Solution Approach 2:
The patent implements periodic updates of localization estimates at optimized intervals rather than continuous processing. The update frequency is adjusted based on zone characteristics, mobility detection, and signal stability, allowing the system to maintain accuracy while reducing processing load during periods when high-frequency updates are not necessary.
Data Source
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AI summary
A method and a device for criterion-based calibration for localization of mobile devices are described. In an example, sensorial data for a zone in the indoor space is obtained. The zone can have a set of factors influencing a behavior of the sensorial data therein. A component of instantaneous localization in the zone is determined based on the sensorial data by simulating variation of the component. The estimation is evaluated based on a ground truth value of the component and, on the basis thereof, a localization criterion is associated with the zone. The localization criterion is indicative of a selectivity in use of the sensorial data for localizing the mobile device in the zone.