Prioritized IMU Selection for Pedestrian Navigation Accuracy
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Solution Overview
Problem
Conventional pedestrian navigation systems face challenges in accurately tracking user position during non-walking activities due to sensor limitations and configurations, leading to performance issues and inaccurate determinations.
Innovation Solution
The implementation of a prioritized inertial measurement unit (IMU) position tracking system, which receives sensor output from multiple IMUs, generates measurement vectors, and selects sensor output based on noise performance to improve navigation accuracy during both walking and non-walking activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional IMU sensors are used for pedestrian navigation, then the system can track user position during walking activities, but the sensors fail to accurately detect non-walking activities due to bandwidth and range limitations
Solution Approach 1:
The patent combines multiple IMU sensors with different characteristics (high bandwidth sensors for walking detection, high range sensors for non-walking detection) into a unified navigation system. The system merges the strengths of different sensors to achieve both accurate walking detection and non-walking activity detection, resolving the contradiction between sensor versatility and measurement precision.
Solution Approach 2:
The system dynamically switches between different IMU sensors based on the detected activity type. During walking activities, the system uses high-bandwidth sensors for precise step detection. During non-walking activities, the system switches to high-range sensors to capture large forces and accelerations. This dynamic adaptation allows the system to maintain high measurement precision across diverse activities.
2Measurement precision
If ZUPT-aided INS is applied to improve navigation accuracy during walking, then position tracking improves, but the system cannot handle non-walking activities where forces exceed sensor bandwidth
Solution Approach 1:
The patent creates a universal navigation system that can handle both walking and non-walking activities using a single integrated framework. The system incorporates multiple sensor types with different bandwidth and range characteristics, allowing it to universally detect all pedestrian activities regardless of whether they are walking, running, or other non-walking movements.
Solution Approach 2:
The system changes the operational parameters of the sensor selection based on activity detection. When non-walking activities are detected (characterized by large forces and accelerations), the system switches to sensors with higher bandwidth and range parameters. This parameter adaptation allows the system to maintain position tracking accuracy across different activity types.
3Measurement precision
If sensor output is selected based on noise performance, then measurement accuracy improves, but the system complexity increases due to multiple IMU sensors
Solution Approach 1:
The patent segments the sensor selection process into distinct stages: first detecting the activity type, then selecting the appropriate sensor based on noise performance and activity characteristics. This segmentation simplifies the overall system architecture by breaking down the complex task of multi-sensor fusion into manageable steps, reducing the practical complexity despite using multiple sensors.
Data Source
AI summary
Process and device configurations are provided for prioritized inertial measurement unit (IMU) position tracking. In one embodiment, a method is provided including receiving sensor output for a plurality of inertial measurement unit (IMU) sensors, and generating a measurement vector for output of the plurality of IMU sensors. The method can also include selecting sensor output of a first IMU sensor, wherein the sensor output for the first IMU sensor is selected using noise performance of sensor output for the first IMU sensor. The method can also include outputting a measurement vector of the first IMU sensor.