Probabilistic Distance Estimation for Mobile Devices
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Solution Overview
Problem
Existing methods for determining the distance between electronic devices, such as smartphones and wearable devices, face challenges in accuracy, especially when GPS signals are unavailable or of low quality, leading to inaccurate location determination.
Innovation Solution
An electronic device equipped with a tracking unit, a determination unit, and an action unit, which uses a probabilistic model to estimate the probability distribution of the distance between devices based on their positions, allowing for accurate distance calculation even in GPS-deprived areas through the integration of position estimation from various sensors and wireless communication data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If GPS-based position estimation is used to determine device distance, then the method is simple and widely applicable, but the measurement precision deteriorates in GPS-deprived areas or when GPS signals are weak
Solution Approach 1:
The patent combines multiple position estimation techniques (GPS, Wi-Fi, cellular, Bluetooth) into a unified probabilistic framework. The determination unit integrates position data from various sources to calculate a probability distribution of device distance, rather than relying on a single method. This merging of multiple estimation approaches resolves the contradiction by maintaining ease of implementation while improving accuracy in GPS-deprived areas.
Solution Approach 2:
The system dynamically changes the weighting and reliability parameters of different position estimation methods based on environmental conditions. When GPS signals are weak or unavailable, the probabilistic model automatically adjusts to rely more heavily on alternative sources like Wi-Fi triangulation or cellular tower positioning. This parameter adaptation allows the system to maintain measurement precision across varying conditions without complicating the overall implementation.
2Measurement precision
If multiple position estimation methods are integrated to improve distance accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent introduces a probabilistic model as an intermediary layer between raw position data from multiple sources and the final distance determination. This intermediary framework systematically integrates GPS, Wi-Fi, cellular, and Bluetooth data while managing the complexity through standardized probability calculations. The determination unit uses this intermediary model to synthesize information from various sensors without requiring complex custom integration logic for each sensor type.
Solution Approach 2:
The probabilistic determination unit is designed as a universal component that can process position data from any source (GPS, Wi-Fi, cellular, Bluetooth) through a common mathematical framework. This multi-functional approach allows the system to handle multiple position estimation methods without proportionally increasing complexity, as the same core algorithm processes all input types. The action unit then universally applies the probability distribution to trigger appropriate actions regardless of which specific sensors provided the data.
Data Source
AI summary
An electronic device includes a tracking unit that tracks a movement of a first mobile device and a movement of a second mobile device, based on a position of the first mobile device and a position of the second mobile device received from a position estimation unit; a determination unit that determines a probability distribution of a distance between the first mobile device and the second mobile device, based on a probabilistic model, and the position of the first mobile device and the position of the second mobile device; and an action unit that performs action, based on the determined probability distribution of the distance.