Airliner Motion Detection via Acceleration Spread Waveform
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Solution Overview
Problem
There is a need for a simple, accurate, and power-efficient mechanism to automatically disable mobile telephony features on personal electronic devices during airliner takeoff and enable them upon landing, due to concerns about interference with aircraft navigation and passenger disruption.
Innovation Solution
The system uses triaxial accelerometer data to generate scalar acceleration signals, filters out high-frequency noise, and processes these signals to create an acceleration spread waveform, which is compared to predetermined patterns to identify airliner motion events like takeoff and landing, thereby controlling mobile device telephony features accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex detection mechanisms are used to accurately identify airliner motion events, then detection accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent segments the acceleration signal processing into distinct stages: raw accelerometer data acquisition, scalar acceleration computation, filtering, and pattern matching. This segmentation allows each stage to be optimized independently, maintaining detection accuracy while reducing overall system complexity through modular processing steps.
Solution Approach 2:
The patent extracts only the essential features from the acceleration signal that are necessary for detecting airliner motion events. By computing scalar acceleration from triaxial data and applying targeted filtering, the system extracts relevant motion patterns while discarding redundant information, thereby simplifying the detection mechanism without compromising accuracy.
2Measurement precision
If complex detection mechanisms are used to accurately identify airliner motion events, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The patent applies partial action by processing only the necessary portion of the acceleration signal. Instead of analyzing all raw accelerometer data in full detail, the system computes scalar acceleration and applies filtering only where needed for pattern recognition, thereby reducing computational load and power consumption while maintaining sufficient detection accuracy.
Solution Approach 2:
The patent extracts only the essential features from the acceleration signal that are necessary for detecting airliner motion events. By computing scalar acceleration from triaxial data and applying targeted filtering, the system extracts relevant motion patterns while discarding redundant information, thereby simplifying the detection mechanism without compromising accuracy.
3Use of energy by moving object
If simple detection mechanisms are used, then power consumption is reduced, but detection accuracy and reliability decrease
Solution Approach 1:
The patent incorporates feedback through pattern matching against predetermined acceleration patterns characteristic of airliner motion events. The system continuously compares processed acceleration signals against known takeoff and landing patterns, adjusting detection decisions based on the degree of match. This feedback mechanism ensures reliable detection while maintaining efficient processing.
Solution Approach 2:
The patent applies preliminary action by pre-defining acceleration patterns characteristic of airliner takeoff and landing events. These predetermined patterns are established beforehand, allowing the system to quickly compare incoming acceleration data against known signatures without requiring complex real-time analysis, thereby maintaining reliability with reduced computational overhead.
4Measurement precision
If continuous monitoring of acceleration signals is performed, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The patent applies periodic action by processing acceleration signals in discrete time windows rather than continuously analyzing every data point. The system computes scalar acceleration, applies filtering, and performs pattern matching at regular intervals, which maintains detection accuracy for motion events while significantly reducing average power consumption compared to continuous full-signal processing.
Data Source
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AI summary
Identifying an airliner motion event at a mobile device may utilize, for example, one or more accelerometers, an acceleration feature extractor, and a motion event identification processor. The one or more accelerometers may be configured to output calibrated triaxial accelerometer data. The acceleration feature extractor may be configured to determine scalar acceleration signals from the calibrated triaxial accelerometer data, to filter the scalar acceleration signals to reduce high frequency noise, and to process the filtered scalar acceleration signals to generate an acceleration spread waveform. The motion event identification processor may be configured to compare the acceleration spread waveform to one or more predetermined patterns characteristic of an airliner motion event, and to identify an airliner motion event based on whether the comparing results in a substantial match.