Motion Direction Estimation Using Adaptive Sensor Parameters
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Solution Overview
Problem
Existing mobile devices face challenges in accurately determining motion direction without GPS or external signals, leading to decreasing reliability over time in dead-reckoning applications due to uncertainties in motion direction and position.
Innovation Solution
A method and system that utilize acceleration data from sensors to identify use cases and select appropriate parameters for calculating an estimated motion direction, incorporating orientation data and step tracking to improve accuracy and reliability, and enabling network interface activation when reliability falls below a threshold for external calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dead-reckoning is used to determine motion direction without GPS or external signals, then the mobile device can operate autonomously in areas without external signals, but the reliability of motion direction estimation decreases significantly over time
Solution Approach 1:
The system continuously monitors the reliability of motion direction estimation and uses this feedback to dynamically adjust operational parameters. When reliability falls below a threshold, the system activates network interfaces to obtain external calibration data, creating a closed-loop feedback mechanism that maintains estimation accuracy while enabling autonomous operation in signal-denied areas.
Solution Approach 2:
The patent implements dynamic switching between autonomous dead-reckoning mode and external signal-based mode based on real-time reliability assessment. The system adapts its operational state dynamically, transitioning between these modes to maintain optimal performance while balancing autonomy with accuracy requirements.
2Reliability
If network interfaces are continuously activated for external calibration, then motion direction accuracy can be maintained, but power consumption increases
Solution Approach 1:
Instead of continuously activating network interfaces, the system employs periodic monitoring of estimation reliability and activates external calibration only when necessary. This periodic action pattern reduces power consumption while maintaining accuracy thresholds, as the network interface remains dormant during periods of sufficient autonomous estimation reliability.
Solution Approach 2:
The system performs self-assessment of estimation reliability using internal sensors and algorithms, determining when external calibration is needed without continuous external intervention. This self-service capability allows the system to minimize network usage and power consumption while maintaining operational accuracy.
3Measurement precision
If multiple sensors and parameters are processed to improve motion direction estimation, then accuracy increases, but device complexity increases
Solution Approach 1:
The system segments the motion estimation process into distinct functional modules: acceleration data processing, orientation data processing, reliability assessment, and calibration coordination. This segmentation allows complex multi-sensor processing to be divided into manageable tasks that can be executed efficiently by existing mobile device components without requiring additional hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the reliability and accuracy of motion direction estimation in mobile devices, allowing for more precise tracking of motion even without GPS or external signals, and optimizes power usage by disabling unnecessary network interfaces.
Implementation Method 1
obtaining acceleration data for a mobile device in each of one or more directions
Implementation Method 2
The orientation data can include one or both of gyroscopic data and magnetometer data
Implementation Method 3
The orientation data can include one or both of gyroscopic data and magnetometer data
Data Source
AI summary
This disclosure provides devices, computer programs and methods for determining a motion direction. In one aspect, a mobile device includes one or more sensors configured to measure acceleration data in each of one or more directions. The mobile device also includes one or more processors and a memory storing instructions that, when executed by the one or more processors, implement a motion direction estimation module. The motion direction estimation module is configured to identify a use case for the mobile device based at least in part on the acceleration data. The motion direction estimation module also is configured to select a set of one or more parameters based on the identified use case. The motion direction estimation module is further configured to calculate an estimated motion direction of the mobile device based on the acceleration data and the respective set of parameters corresponding to the identified use case.