Indoor Positioning via Mesh Network Fingerprinting
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Solution Overview
Problem
Existing indoor positioning systems using fingerprinting techniques face challenges in providing accurate, real-time, and robust location estimation due to the need for high granularity received signal strength indicators (RSSIs) in wireless networks.
Innovation Solution
An indoor positioning system comprising multiple access points that collect and share fingerprinting data through a learning phase, where users mark landmarks and track RSSIs, and a positioning phase that compares measured RSSIs to a database to determine the device's location, utilizing a mesh network for connection services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting techniques are used for indoor positioning, then location estimation can be provided, but high granularity RSSIs are required which makes real-time accurate positioning problematic
Solution Approach 1:
The system performs a learning phase beforehand to collect and store RSSI fingerprints at multiple locations in the environment. This pre-collected data is stored in a database, allowing the positioning phase to quickly compare current RSSI measurements against the pre-stored fingerprints without requiring real-time high-granularity measurements, thus achieving accurate positioning without the complexity of continuous high-precision measurements
2Measurement precision
If high granularity RSSIs are collected to improve positioning accuracy, then location estimation precision improves, but the system becomes more complex and real-time performance deteriorates
Solution Approach 1:
RSSI fingerprints are collected and stored in advance during a learning phase, creating a database of location-specific signal characteristics. During the positioning phase, the system simply compares current RSSI measurements against this pre-built database using pattern matching, which is computationally efficient and enables real-time positioning without requiring complex real-time high-granularity measurements
3Measurement precision
If fingerprinting database is maintained with high granularity data, then positioning accuracy improves, but data collection and processing complexity increases
Solution Approach 1:
The system collects fingerprinting data during a dedicated learning phase before actual positioning operations. This pre-collection approach allows comprehensive data gathering without impacting real-time positioning performance, and the data can be organized and stored in a structured database format that facilitates efficient querying and comparison during positioning operations
Data Source
AI summary
A method and a system for indoor positioning within a wireless mesh-type network can be applied where a plurality of access points are located in an indoor space. According to the method, any one access point of the plurality of access points receives a request for indoor positioning transmitted by a portable device, wherein the request comprises received signal strength indications (RSSIs) measured and collected by the portable device. The access point compares the measured and collected RSSIs of the request to RSSIs stored in a fingerprinting database to determine a fingerprinting position using a closest match method and the access point provides, to the portable device, the determined fingerprinting position.