Iterative Wireless Location Estimation with Transmit Power Correction
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Solution Overview
Problem
Existing location estimation methods for wireless devices are prone to errors due to uncertainty in transmit power, especially in scenarios where the transmit power is variable or unavailable, leading to incorrect location determination.
Innovation Solution
An apparatus and method that determine a wireless device's location by receiving signal strength measurements from multiple known locations, estimating the transmit power, and iteratively refining both the location and transmit power based on these measurements, using a path loss model and heatmap differences to correct for obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If transmit power is assumed to be fixed or predefined, then location estimation can be performed with simpler methods, but location estimation accuracy deteriorates when transmit power is variable or unavailable
Solution Approach 1:
The system performs iterative refinement where the initial location estimate is used to determine transmit power, which then feeds back to improve the location estimate. This feedback loop continues until convergence, allowing the system to adapt to variable transmit power while maintaining reasonable computational complexity.
Solution Approach 2:
The system first performs a preliminary location estimation using available information (signal strengths and predefined transmit power assumptions). This preliminary result serves as a starting point for subsequent refinement steps, enabling the system to make progress even with incomplete information.
2Measurement precision
If iterative refinement of location and transmit power is performed, then location estimation accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system dynamically adjusts the refinement process by iterating between location estimation and transmit power determination until convergence is achieved. This dynamic approach allows the system to achieve high accuracy while adapting the computational effort to the specific scenario, avoiding unnecessary iterations when the solution converges quickly.
3Device complexity
If standard path loss models are used without obstacle correction, then the estimation process is simpler, but accuracy deteriorates in environments with obstacles
Solution Approach 1:
The system applies local corrections to the path loss model based on obstacle detection. Instead of using a uniform model throughout the environment, it adjusts the path loss characteristics in specific local areas where obstacles are detected, improving accuracy without requiring complete re-modeling of the entire environment.
Solution Approach 2:
The system changes the path loss model parameters dynamically based on detected obstacles. When obstacles are present, the path loss exponent and other parameters are adjusted to reflect the additional signal attenuation, allowing the model to adapt to different environmental conditions while maintaining a relatively simple overall structure.
Data Source
AI summary
In an example embodiment, there is disclosed herein, an apparatus comprising an interface and location determination logic coupled with the interface. The location determination logic receives data representative of measured signal strengths for a wireless device from a plurality of receiving devices at known locations via the interface. The location determination logic determines an estimated location based on the measured signal strengths and a first transmit power for the wireless device. The location determination logic determines a revised transmit power for the wireless device based on the measured signal strengths from the plurality of devices at known locations and the estimated location. The location determination logic determines a revised estimated location based on the measured signal strengths and the revised transmit power for the wireless device.