Mutual Signal Assisted Tag Positioning in WLAN
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Solution Overview
Problem
Existing location estimation techniques for tags in WLAN environments face challenges such as high battery consumption, inaccurate positioning due to random fluctuations and obstacles, and insufficient base station coverage, leading to unreliable location determination.
Innovation Solution
A method involving a positioning engine that models location-dependent physical quantities using a data model, receives observations from signalling devices associated with target objects, and sends positioning-assisting signals to improve location probability distributions for accurate tag positioning, even in environments with limited base station coverage or obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the tag performs frequent signal strength measurements from multiple base stations to improve positioning accuracy, then positioning reliability is improved, but battery consumption increases
Solution Approach 1:
The tag alternates between sleep mode and active scanning mode, performing measurements only at periodic intervals rather than continuously. This reduces energy consumption while maintaining positioning functionality through scheduled wake-up periods for signal strength measurements.
Solution Approach 2:
The system uses mutual assistance between tags and base stations, where tags can act as positioning sources for other tags. This self-service mechanism reduces the need for each tag to independently perform extensive measurements, thereby lowering individual battery consumption while maintaining collective positioning reliability.
2Measurement precision
If the tag remains in active mode to enable frequent positioning updates, then positioning accuracy is improved, but battery lifetime decreases
Solution Approach 1:
The tag implements periodic scanning cycles with configurable intervals, remaining in sleep mode between scans to conserve battery. During active scan periods, the tag performs comprehensive measurements to maintain accuracy, then returns to sleep mode to extend battery lifetime.
Solution Approach 2:
The system performs preliminary positioning assessments using available data to determine when full scanning is necessary. This preliminary action allows the tag to skip unnecessary full measurement cycles, extending battery life while maintaining positioning accuracy when needed.
3Reliability
If more base stations are added to improve positioning coverage and accuracy, then positioning reliability is improved, but device complexity and cost increase
Solution Approach 1:
The system enables base stations to serve dual purposes: their primary communication function and a secondary positioning function. By utilizing existing base station infrastructure for both WLAN communication and positioning signals, the system improves positioning coverage without adding dedicated positioning infrastructure, thereby avoiding increased device complexity.
Solution Approach 2:
Tags utilize signals from existing base stations and other tags for mutual positioning assistance. This self-service approach allows the system to achieve improved coverage and accuracy by leveraging available infrastructure rather than requiring additional dedicated positioning base stations.
4Measurement precision
If the tag scans all pre-configured channels to ensure complete base station observations, then positioning accuracy is improved, but battery consumption and scanning time increase
Solution Approach 1:
The tag performs partial channel scanning by selecting a subset of pre-configured channels based on current positioning needs and available time resources. This partial action approach maintains acceptable positioning accuracy while reducing the total scanning time and associated battery consumption compared to scanning all channels.
Solution Approach 2:
The system implements periodic channel scanning with configurable duty cycles, scanning channels at intervals rather than continuously. This periodic approach reduces scanning time and energy consumption while maintaining positioning accuracy through scheduled measurement opportunities.
Data Source
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AI summary
Location estimation for first target object (TO1) assisted by second target object (TO2), which have co-located signalling devices (STR1, STR2). A positioning engine (PE) employs a data model (DM) of a location-dependent physical quantity and determines location probability distributions for the target objects. One signalling device (STR2) sends positioning-assisting signals to the other (STR1 ) which makes observations from it. The positioning engine (PE) uses observations on the physical quantity and the positioning-assisting signals to make a quantity observation set, and determines location probability distributions (LPD1, LPD2) for the target objects. The positioning engine (PE) determines an updated first location probability distribution (LPD1') based on the location probability distributions and the positioning-assisting observation set. The positioning engine determines the location estimate for the first target object based on the updated first location probability distribution and triggers a physical action based on the location estimate for the first target object.