Sensor Calibration via Neural Network Reconstruction
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Solution Overview
Problem
Calibrating multiple sensors in a multi-sensor system is complex due to unknown local relations, sensor offsets, noise, and time domain mismatches, making high precision calibration challenging.
Innovation Solution
An LSTM encoder/decoder neural network with an attention map is used to automatically calibrate sensors by minimizing reconstruction errors between sensor trajectories and a reference trajectory, adapting calibration vectors and optimizing network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration of sensors is performed, then calibration accuracy can be improved, but calibration time and complexity increase significantly
Solution Approach 1:
The system performs self-calibration using an encoder-decoder neural network that automatically processes sensor trajectories and determines calibration parameters without human intervention. The neural network encodes the sensor trajectories, processes them through learned representations, and decodes the calibration parameters, enabling the system to calibrate itself autonomously while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical calibration processes with an automated computational system based on neural networks. Instead of physical adjustment and manual measurement, the system uses deep learning models to automatically compute calibration parameters from sensor data, significantly reducing time and complexity.
2Ease of manufacture
If conventional calibration methods are used, then the process is simple to implement, but handling of noise and time domain mismatches results in poor calibration precision
Solution Approach 1:
The system performs preliminary encoding of sensor trajectories into latent representations before calibration computation. The encoder processes the raw sensor data and time domain information in advance, creating compressed representations that capture essential features and relationships, which then facilitates more accurate calibration parameter determination.
Solution Approach 2:
The patent introduces an intermediate latent space between the input sensor trajectories and the output calibration parameters. The encoder-decoder architecture creates this intermediary representation layer, which acts as a mediator that processes and reconciles noisy sensor data, handles time domain mismatches, and produces accurate calibration parameters.
3Device complexity
If sensor trajectories with noise and time delays are processed using traditional methods, then computational complexity remains low, but calibration accuracy deteriorates
Solution Approach 1:
The system employs dynamic processing through the neural network that adapts to varying input conditions. The encoder-decoder architecture dynamically processes sensor trajectories, adjusting its internal representations based on the specific characteristics of the input data, including noise patterns and time delays, thereby maintaining high accuracy across different scenarios.
Solution Approach 2:
The patent transforms the calibration problem by changing the parameter space through encoding into a latent representation. Instead of directly processing raw sensor parameters, the system encodes them into a transformed space where noise and time delays are better handled, then decodes the calibration parameters from this optimized representation.
Data Source
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AI summary
The invention relates to a method for calibrating a sensor (2) with respect to a reference trajectory (Tref) indicating a number of timely successive data values using an encoder/decoder neural network (10) with an encoder function (gencoder) and a successive decoder function (fdecoder), comprising the steps of: - Training in a first training phase the encoder function (gencoder) and the decoder function (fdecoder) of the encoder/decoder neural network (10) with respect to at least one reference trajectory (Tref) so that a reconstruction error of the reference trajectory (Tref) at an input of the encoder function (gencoder) and a reconstructed reference trajectory (Tref) at an output of the decoder function (fdecoder) is minimized, - Training in a second training phase a calibration vector (WTt) with respect to a sensor trajectory (T) so that a reconstruction error between an output sensor trajectory (Tb, Tc, Td) and a corresponding reference trajectory (Tref) is minimized, wherein the output sensor trajectory (Tb, Tc, Td) is obtained by applying the calibration vector (WTt) on the sensor trajectory (Tb, Tc, Td) and by applying the encoder function (gencoder) and decoder function (fdecoder) on the the calibrated sensor trajectory; - Applying the calibration vector (WTt) to calibrate the sensor.