Wireless Signal Strength Probability Matrix for Location Estimation
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Solution Overview
Problem
Existing methods for estimating the location of mobile devices and wireless access points are limited, especially in environments with obstructions and for devices without GPS capabilities, and they often consume significant battery power.
Innovation Solution
A method that involves storing samples of mobile device locations and signal strengths from access points in a probability matrix, updating these probabilities based on additional samples, and using them to estimate the location of both mobile devices and access points through probability distribution functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS technology is used to determine location, then location accuracy is improved, but battery consumption increases
Solution Approach 1:
The patent introduces wireless signal strength data as an intermediary measurement to estimate location without directly using GPS. By measuring signal strengths from known access points and comparing them against a probability matrix, the system derives location information through an indirect measurement path that consumes less energy than direct GPS positioning.
Solution Approach 2:
The patent creates a probabilistic model (copy) of the physical space based on signal strength characteristics. Instead of directly relying on GPS coordinates, the system builds a probability matrix that replicates spatial relationships through signal measurements, allowing location estimation to proceed via this copied representation rather than direct GPS dependency.
2Loss of time
If GPS is used frequently to update location dynamically, then location freshness is improved, but battery consumption increases
Solution Approach 1:
The patent implements periodic sampling of wireless signal strengths at predetermined intervals to update location estimates. This periodic measurement approach maintains location freshness by regularly updating the probability matrix with new signal data, while avoiding continuous GPS activation that would drain battery power.
Solution Approach 2:
The system maintains continuous location tracking capability by continuously monitoring wireless signal strengths in the background, even when GPS is not actively engaged. This continuous passive measurement keeps the probability matrix updated without the energy cost of frequent GPS activations, ensuring location information remains fresh.
3Use of energy by moving object
If signal strength measurements are used to estimate location, then battery consumption is reduced, but location accuracy worsens in obstructed environments
Solution Approach 1:
The patent merges multiple signal strength measurements from different access points into a unified probability matrix. By combining data from multiple sources and integrating it with existing probabilistic information through Bayesian updating, the system compensates for obstructions that may block individual signals, maintaining accuracy without requiring additional energy-intensive measurements.
Solution Approach 2:
The system implements feedback through iterative Bayesian updating of the probability matrix. Each new signal strength measurement is processed to update the probability distribution, with the updated matrix feeding back into subsequent location estimations. This feedback loop allows the system to adapt to changing environmental conditions including obstructions, improving robustness while maintaining low power consumption.
Data Source
AI summary
According to an embodiment, a method of estimating a location of an access point is provided. The method includes storing a first and a second sample respectively received from a first and a second mobile device, both samples including a location of their respective mobile devices, an access point identifier from the access point, and a value corresponding to a signal strength of a signal from the access point measured at their respective mobile device. The cells of a matrix are populated with probabilities based on mobile device locations and the signal strength values. Finally, the location of the access point is estimated based on the matrix.