Trajectory Estimation Using Rotation Matrix Coefficients
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Solution Overview
Problem
Existing technologies face challenges in efficiently determining the location and trajectory of devices or users with high costs in terms of processor cycles, energy, and memory usage, while also requiring significant semiconductor real estate.
Innovation Solution
The implementation of a system that analyzes rotation matrix coefficients in conjunction with stepping information to estimate device position and trajectory, utilizing a motion processing unit (MPU) with sensors like gyroscopes, accelerometers, and magnetometers, and employing sensor fusion algorithms to process data efficiently, thereby reducing processing overhead and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional location determination methods are used, then location accuracy is achieved, but processor cycles and energy consumption increase significantly
Solution Approach 1:
The system segments the location determination task by using multiple sensors (accelerometer, gyroscope, magnetometer) to collect different types of motion data, then processes these segments through sensor fusion algorithms to achieve accurate trajectory estimation without relying on energy-intensive single-method approaches
Solution Approach 2:
The system changes the parameters of motion analysis by utilizing rotation matrix coefficients derived from sensor data, transforming raw sensor readings into meaningful trajectory information through mathematical transformations that reduce overall processing energy requirements
2Measurement precision
If comprehensive sensor processing is implemented, then trajectory estimation accuracy improves, but device complexity increases
Solution Approach 1:
The motion processing unit is designed with multi-functionality, integrating accelerometer, gyroscope, and magnetometer processing capabilities within a single device that can perform multiple functions including motion detection, orientation tracking, and trajectory estimation, thereby managing complexity through functional integration
Solution Approach 2:
Rotation matrix coefficients serve as an intermediary representation that bridges raw sensor data and final trajectory estimates, simplifying the processing pipeline by providing a standardized intermediate form that facilitates accurate trajectory calculation without requiring complex direct processing of all sensor inputs
3Reliability
If high-precision location tracking is performed continuously, then location knowledge is maintained, but processor cycles and memory usage increase
Solution Approach 1:
The system implements periodic action by updating trajectory estimates at optimized intervals based on motion detection thresholds and confidence levels, maintaining continuous location knowledge through periodic corrections rather than constant high-intensity processing, thereby improving processing efficiency while preserving reliability
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
This approach allows for accurate and efficient determination of device location and trajectory, minimizing the need for external amplification and reducing energy consumption, while integrating multiple sensors into a small, economical package, thus overcoming the limitations of conventional methods.
Implementation Method 1
utilizing a motion processing unit (MPU) with sensors like gyroscopes
Implementation Method 2
sensors like gyroscopes, accelerometers, and magnetometers
Implementation Method 3
sensors like gyroscopes, accelerometers, and magnetometers
Data Source
AI summary
A system and method for efficiently determining trajectory and/or location of a device (or user thereof). In a non-limiting example, rotation matrix coefficients may be analyzed in conjunction with stepping information to determine device trajectory and/or location. The system and method may, for example, be implemented in a MEMS sensor system, for example comprising a MEMS gyroscope, MEMS accelerometer, MEMS compass and/or MEMS pressure sensor.