Mobile Device Position Estimation Using Probabilistic Signal Search
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Solution Overview
Problem
Mobile devices face challenges in efficiently estimating their position using wireless signals due to varying accuracy, power consumption, and environmental factors, as different signal sets provide different outcomes in terms of time-to-fix, power usage, and accuracy.
Innovation Solution
The implementation of probabilistic models that update based on historical statistical information to determine the best approach for selecting wireless signals for position estimation, considering factors like time-to-fix, power consumption, and accuracy, and leveraging available signal strengths and patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple wireless signal sets are searched for position estimation, then positioning accuracy is improved, but power consumption increases
Solution Approach 1:
The system dynamically changes search parameters (signal types, frequency ranges, geographic areas) based on probabilistic models that predict likelihood of successful acquisition. This allows the device to adjust the extent of signal searching according to predicted outcomes, improving accuracy when needed while reducing power consumption when predictions indicate low success probability
Solution Approach 2:
The patent implements dynamic adaptation of the signal search strategy by continuously updating probabilistic models with historical statistical information. The system transitions from static search approaches to dynamic ones where search behavior adapts in real-time based on changing environmental conditions and historical patterns, resolving the contradiction between thorough searching and power efficiency
2Measurement precision
If comprehensive signal searching is performed, then position estimation accuracy is improved, but time to obtain position fix increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing probabilistic models based on historical statistical information before actual position estimation is needed. These pre-computed models enable rapid decision-making during real-time operation, allowing the device to quickly determine which signal sets are most likely to succeed without performing exhaustive searches, thus reducing time to fix while maintaining accuracy
Solution Approach 2:
The patent dynamically adjusts search parameters based on probabilistic predictions, changing the scope and intensity of signal searching according to predicted success probabilities. This allows the system to focus computational resources on the most promising signal sets, improving accuracy efficiently while minimizing the time required to obtain a position fix
3Productivity
If probabilistic models are updated with historical data, then search strategy optimization is improved, but processing complexity increases
Solution Approach 1:
The system implements self-service by automatically updating its own probabilistic models using historical statistical information without requiring external intervention or complex manual configuration. The device autonomously learns from past performance data and adapts its search strategies, improving productivity through self-optimization while managing processing complexity through automated rather than manual processes
Solution Approach 2:
The patent incorporates feedback mechanisms where historical results from position estimation attempts are fed back into the probabilistic models. This continuous feedback loop allows the system to refine its search strategies based on actual performance, improving optimization over time. The feedback-based approach manages complexity by using systematic data collection and model updating rather than ad-hoc adjustments
Data Source
AI summary
Disclosed are methods, techniques and/or systems for selecting and/or determining a strategy and/or approach for searching for signals at a mobile device. Characteristics of and/or information obtained from such searched signals may be used in estimating a location of the mobile device. In one particular example, a strategy and/or approach for searching for wireless signals may be based, at least in part, on an availability of resources at a mobile device.