Wireless Channel Localization With IMU Pseudo-Label Training
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Solution Overview
Problem
Conventional machine learning-based positioning systems rely on dense position labels that are often impractical or impossible to obtain, and IMU information is noisy, leading to inaccurate location predictions.
Innovation Solution
Generate pseudo-labels using a forward-backward double integration process based on inertial measurement unit (IMU) data and sparse control points, combined with wireless channel measurements to train machine learning models for accurate location estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense position labels are used for training machine learning models, then prediction accuracy is improved, but data collection complexity and cost increase significantly
Solution Approach 1:
The system uses IMU data to self-generate position labels without requiring external dense labeling. The device autonomously creates training data by integrating its own motion sensor measurements, eliminating the need for complex manual annotation processes while providing sufficient training samples for the machine learning model.
Solution Approach 2:
IMU data serves as an intermediary between the physical motion and the position labels needed for training. Instead of directly obtaining complex position labels through manual annotation, the system uses IMU measurements as a intermediate representation that can be automatically converted into position information for training purposes.
2Ease of operation
If IMU data is used for position estimation, then data collection is simplified, but noise in IMU information reduces prediction accuracy
Solution Approach 1:
The system uses feedback from multiple data sources (wireless channel measurements, visual data, and IMU data) to continuously refine position estimates. The machine learning model learns to weigh and combine these different inputs, using feedback from accurate measurements to correct IMU drift and noise, thereby maintaining high accuracy while keeping data collection simple.
Solution Approach 2:
The system creates a composite training dataset that combines IMU data with wireless channel measurements and visual data. This composite approach leverages the ease of IMU data collection while compensating for its noise through the complementary information from other sensors, resulting in robust position estimation.
3Measurement precision
If manual position labeling is performed, then label accuracy is improved, but training data generation time increases
Solution Approach 1:
The system performs preliminary action by pre-processing IMU data into position estimates before the actual training process. This preliminary integration of IMU measurements creates ready-to-use position labels that can be immediately used for training, eliminating the need for time-consuming manual annotation while providing accurate enough labels for effective model training.
Solution Approach 2:
The system self-generates position labels by autonomously processing its own IMU sensor data. Instead of requiring external manual labeling, the device independently creates its training dataset by integrating motion sensor information, significantly reducing the time required for data preparation while maintaining sufficient label accuracy for training purposes.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A sequence of data records is accessed, each data record comprising wireless channel measurements and inertial measurement unit (IMU) data. Known position information corresponding to at least a first data record is accessed. A first sequence of positions is determined by processing the sets of IMU data and known position information using a forward operation. A second sequence of positions is determined by processing the sets of IMU data and known position information using a backward operation. An IMU adjustment parameter is generated using the first and second sequences of positions. A pseudo-label is generated for a second data record using the IMU adjustment parameter and the sets of IMU data. A machine learning model is trained, using the second data record and the pseudo-label, to predict positions using one or more wireless channel measurements.