Indoor-Outdoor State Detection Using Multi-Sensor Confidence Fusion
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Solution Overview
Problem
Existing systems fail to accurately determine whether a computing device is indoors or outdoors, especially in complex environments with open structures or when in motion, due to the time it takes for environmental sensors to reach a high-confidence estimate, which can lead to inaccurate statistical confidence levels.
Innovation Solution
A system that uses a combination of sensor data analytics, including GNSS RSL measurements, light spectrum analysis, temperature, humidity, and radio signal data, along with machine learning models to determine the environmental state of a computing device with high confidence, even in dynamic conditions, by adjusting confidence levels based on velocity and using multiple sensor types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If environmental sensors are used to determine indoor/outdoor state, then environmental detection capability is improved, but the time to reach high-confidence estimate increases
Solution Approach 1:
The patent combines data from multiple sensor types (GNSS, barometric pressure, temperature, humidity, light sensors, motion sensors) to determine environmental state. This multi-sensor fusion approach allows the system to reach high-confidence estimates faster by cross-validating signals across different measurement modalities, resolving the contradiction between accuracy and response time.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and pre-processing sensor data even before a definitive indoor/outdoor determination is needed. This includes initializing sensor readings, establishing baseline environmental conditions, and pre-computing confidence values so that when environmental state determination is required, the system can quickly reference pre-analyzed data rather than starting from scratch.
2Measurement precision
If multiple sensor types are used to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the environmental detection function into distinct sensor modules (GNSS receiver, barometric pressure sensor, temperature sensor, humidity sensor, light sensor, motion sensor), each responsible for specific measurements. This modular segmentation allows the system to achieve high detection accuracy through multi-sensor fusion while managing complexity by organizing sensors into independent, maintainable units with clearly defined roles.
Solution Approach 2:
The system implements universal data processing logic that handles inputs from multiple sensor types through a unified analysis framework. The same computational algorithms and confidence value calculation mechanisms process data from different sensor sources, allowing the system to achieve measurement precision through diversity while avoiding the complexity of separate processing pipelines for each sensor type.
3Adaptability or versatility
If the device is in motion, then operational versatility is improved, but measurement precision decreases due to dynamic conditions
Solution Approach 1:
The patent implements dynamic confidence value adjustment based on device motion state. When motion sensors detect that the device is in motion, the system dynamically modifies the confidence value calculation to account for the reduced reliability of environmental measurements. This allows the system to maintain operational versatility in mobile conditions while compensating for the decrease in measurement precision through adaptive confidence scoring.
Solution Approach 2:
The system uses feedback from motion sensors to continuously adjust the confidence values assigned to environmental measurements. When motion is detected, the feedback mechanism reduces the weight of environmental sensor data in the overall determination, compensating for the decreased precision in dynamic conditions and maintaining accurate indoor/outdoor state detection despite device mobility.
Data Source
AI summary
Systems and methods are provided for deterministically estimating whether the location of a computing device that is fixed or mobile is inside a fully enclosed building or not (e.g., fully or partially indoors/outdoors). Various environments are supported by the substance of the disclosure, including fully or partially indoor and outdoor environments.