Zone-Based Indoor Localization Using Selective Sensor Data
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Solution Overview
Problem
Existing indoor navigation systems for mobile devices face challenges in accurately localizing devices within indoor spaces due to unreliable sensor data and high computational resource consumption, especially in environments with varying environmental characteristics such as enclosed or partially enclosed areas with different layouts and elevations.
Innovation Solution
The method involves segregating indoor spaces into zones based on environmental factors and using predetermined behavioral patterns of sensor data to selectively utilize reliable sensor data for localization, assigning weightages to sensor data based on reliability scores for each zone, thereby optimizing localization accuracy and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based navigation systems are used for indoor positioning, then outdoor positioning accuracy is improved, but positioning reliability deteriorates in enclosed or partially enclosed areas
Solution Approach 1:
The system segments the indoor space into multiple zones with different environmental characteristics (e.g., open areas, corridors, rooms). Each zone has pre-determined reliable sensor data types based on its characteristics. The mobile device identifies which zone it is in and selectively uses appropriate sensor data, thereby resolving the contradiction by adapting to local environmental conditions rather than using a uniform approach throughout the entire indoor space.
Solution Approach 2:
Different zones within the indoor space are assigned different localization criteria based on their specific environmental characteristics. For example, magnetic field data may be reliable in open areas but unreliable in corridors with metallic structures. The system applies local quality by tailoring the sensor data selection to each specific zone's characteristics, ensuring reliable positioning in each local environment.
2Measurement precision
If all sensor data types are used for localization, then positioning accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system extracts and uses only the reliable sensor data types for each specific zone, discarding unreliable data types. For example, in a zone where magnetic field data is unreliable due to metallic structures, the system extracts and uses only accelerometer and gyroscope data. This extraction of only necessary reliable data reduces computational resource consumption while maintaining localization accuracy.
Solution Approach 2:
Instead of using all sensor data types uniformly (excessive action), the system uses only the subset of sensor data types that are reliable for the current zone (partial action). This partial usage of sensor data reduces computational overhead while maintaining sufficient localization accuracy for each specific environmental context.
3Productivity
If zone-based localization with selective sensor data usage is implemented, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-determining the reliable sensor data types for each zone before the mobile device enters the zone. The zone characteristics and corresponding reliable sensor data mappings are established in advance through calibration processes. When the device enters a zone, it can quickly retrieve and apply the pre-determined localization criteria without performing complex real-time analysis, thereby reducing system complexity while maintaining high localization efficiency.
Data Source
AI summary
A method and a device for zone-based localization of mobile devices are described. In an example, a zone from amongst a plurality of zones of an indoor space in which a mobile device is present is identified. The identification of the can be based on instantaneous localization information obtained from the mobile device. Further, a localization criterion to be employed for localizing the mobile device is determined based on the identified zone. The localization criterion may be indicative of a selectivity in use of sensor data for localizing the mobile device. Subsequently, the mobile device is localized in the indoor space, based on the localization criterion.