Indoor Positioning via Multi-Sensor Fusion and Noise Filtering
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Solution Overview
Problem
Inertial sensors in electronic devices suffer from noise in collected data, leading to low accuracy in indoor positioning.
Innovation Solution
A positioning method and apparatus that collect and filter multiple types of sensor data, such as acceleration and angular velocity, to improve accuracy by counting user steps and correcting position information, utilizing multi-information fusion and noise removal techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial sensors are used for indoor positioning, then positioning functionality is provided, but positioning accuracy deteriorates due to noise in sensor data
Solution Approach 1:
The patent combines multiple types of sensor data (acceleration, angular velocity, and other motion-related sensors) to perform step counting and positioning. By merging data from different sensor sources, the system overcomes the noise limitation of individual sensors and improves overall positioning accuracy through multi-sensor fusion.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes filtering modules and step-counting algorithms. These intermediaries process the raw sensor data to remove noise and extract meaningful motion information, thereby improving positioning accuracy without requiring changes to the sensors themselves.
2Measurement precision
If multiple types of sensor data are collected and filtered, then positioning accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the data processing task into separate modular components: individual filtering modules for each sensor type, a step-counting module, and a positioning module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high positioning accuracy.
Solution Approach 2:
The patent applies preliminary filtering to sensor data before step-counting and positioning calculations. By pre-processing the data to remove noise and extract relevant features in advance, the system reduces the computational complexity of subsequent processing stages while improving positioning accuracy.
Data Source
AI summary
A positioning method is disclosed. The positioning method includes collecting at least two types of sensor data reflecting a motion state of a terminal device in a step-counting period in real time; separately filtering the at least two types of sensor data to obtain filtered sensor data; step-counting a user holding the terminal device based on the filtered sensor data to obtain a distance and a direction of movement of the user in the step-counting period; and correcting current position information of the user based on the distance and the direction of the movement of the user in the step-counting period to implement positioning of the user. The technical solution provided in the present disclosure can be adopted to improve the accuracy of indoor positioning.