Indoor Floor Assignment Using Height Links and Ambient Signals
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Solution Overview
Problem
Current smartphone pressure sensors are not accurate for absolute pressure measurements, making it unreliable to determine floor number or height, and comparisons between different devices are also unreliable, especially in crowdsourced data, which complicates the assignment of trajectory segments to corresponding floors.
Innovation Solution
A method that receives data from a mobile device, segments the trajectory using height data, calculates similarity values based on ambient signal data, groups segments, checks for errors, and iteratively refines the grouping process to accurately assign segments to floors using height links and ambient signal similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pressure sensors are used for absolute pressure measurements to determine floor number, then floor assignment can be performed, but measurement accuracy deteriorates due to environmental pressure changes and sensor noise
Solution Approach 1:
The patent segments trajectories into multiple segments based on height changes, then groups these segments using ambient signal data. This segmentation approach allows the system to process location data in manageable units while compensating for pressure sensor inaccuracies through collective analysis of multiple segments.
Solution Approach 2:
The patent introduces ambient signal data (WiFi, Bluetooth, cellular) as an intermediary to validate and correct floor assignments. These ambient signals serve as a reference framework that compensates for pressure sensor errors, allowing accurate floor determination despite poor absolute pressure measurement accuracy.
2Reliability
If pressure comparisons are performed between different devices to improve accuracy, then measurement reliability may improve, but device complexity increases due to calibration requirements
Solution Approach 1:
The patent enables devices to self-calibrate by comparing their pressure measurements against the collective data from multiple devices and validating against ambient signal patterns. The system automatically performs calibration without requiring manual intervention or complex external calibration equipment.
Solution Approach 2:
The patent creates a universal reference framework using ambient signals that can be used by all devices regardless of their individual pressure sensor characteristics. This multi-functional approach allows the same ambient signal framework to serve as both a location reference and a calibration reference for all devices in the crowd.
3Quantity of substance
If crowd-sourced data from multiple devices is used to improve coverage, then data availability increases, but data quality deteriorates due to sensor variations between devices
Solution Approach 1:
The patent merges data from multiple devices by grouping trajectory segments based on similarities in ambient signal patterns. This combining approach allows the system to aggregate large volumes of crowd-sourced data while maintaining consistency through the unifying ambient signal reference framework.
Solution Approach 2:
The patent transforms the problem by changing from using absolute pressure values (which vary between devices) to using relative height changes combined with ambient signal patterns. This parameter transformation allows consistent floor assignment across all devices regardless of their individual sensor characteristics.
4Measurement precision
If iterative grouping and error checking is performed to improve accuracy, then floor assignment accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary grouping of trajectory segments using ambient signal data before final floor assignment. This preliminary organization reduces the computational complexity of subsequent error checking and validation steps, allowing iterative refinement to proceed more efficiently.
Solution Approach 2:
The patent implements feedback loops where error checking results are used to refine the grouping, which then feeds back into further error checking. This iterative feedback process gradually improves accuracy while the system monitors processing time to balance precision with computational efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively assigns trajectory segments to corresponding floors, even with varying device sensors and environmental conditions, by leveraging relative changes in pressure and ambient signal patterns, enhancing accuracy and reliability in floor assignment.
Implementation Method 1
receiving data corresponding to a trajectory from a mobile device moved along that trajectory, the data comprising height data and ambient signal data
Implementation Method 2
the data comprising height data and ambient signal data
Data Source
AI summary
A method 400 of automatically assigning segments of trajectories 110, 120, 130 to floors of a building 100 comprises receiving 801 data concerning a trajectory from a mobile device 152, the data comprising height data and ambient signal data; segmenting 802 the trajectory using the height data, such that a change in the height data marks an end region of a segment. A change in the height data between adjacent segments is referred to as a height link. The method further comprises calculating 803 similarity values for pairs of segments based on the ambient signal data; using the similarity values, grouping 804 the segments into a plurality of groups based on the ambient signal data; checking 805 for errors in the grouping using the height links; accepting 808a the grouping if the checking does not identify any errors, or re-running 807 the grouping if it does; and, once the grouping is accepted, assigning 809 the groups to corresponding floors.