ToF Phase Correction Using Neural Networks Against Multipath Interference
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Solution Overview
Problem
Multipath interference (MPI) in time-of-flight (ToF) modules distorts phase and amplitude measurements, leading to inaccuracies in distance calculations.
Innovation Solution
A time of flight correction method using a pre-trained neural network that processes measured phases and amplitudes to output phase corrections, which are then converted to corrected ranges, thereby mitigating the effects of MPI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a ToF module measures phases and amplitudes of electromagnetic waves, then distance information can be obtained, but multipath interference distorts the measurements and reduces accuracy
Solution Approach 1:
A neural network model is introduced as an intermediary between the raw phase/amplitude measurements and the final distance calculation. The neural network processes the measured phases and amplitudes, learning to distinguish direct path signals from multipath reflected signals, thereby correcting the measurements and improving accuracy without requiring changes to the physical ToF measurement process
Solution Approach 2:
The patent replaces traditional signal processing methods (mechanical/physical approaches) with a neural network-based computational approach. Instead of using physical filters or mathematical algorithms to separate direct and reflected signals, the system uses a trained neural network that has learned the characteristics of multipath interference patterns, substituting computational intelligence for conventional signal processing techniques
2Device complexity
If traditional signal processing methods are used to correct multipath interference, then computational complexity increases, but the patent uses a neural network to simplify the process
Solution Approach 1:
The neural network model is pre-trained offline using labeled data containing known multipath scenarios. This preliminary training phase allows the network to learn complex interference patterns and correction strategies beforehand, so that during actual operation, only simple forward propagation through the trained network is needed, significantly reducing real-time computational complexity while maintaining high measurement accuracy
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
Improves the accuracy of ToF module measurements by correcting for multipath interference, ensuring precise distance calculations.
Implementation Method 1
A time of flight (ToF) camera, also referred to as a time-of-flight sensor (ToF sensor), is an advanced imaging system designed for measuring distances between the camera and objects in the environment. It operates based on the concept of time-of-flight, which involves calculating the round trip time of an artificial light signal
Implementation Method 2
However, the accuracy of a ToF module suffers from multipath interference (MPI). The MPI might be generated by two reflections of surrounding objects 104. An ideal ToF should be calculated by a path with only one reflection, but multipath waves with two or more reflections are also received by the ToF module 102, thus affecting the accuracy of the TOF calculation
Data Source
AI summary
A time of flight correction method includes measuring a plurality of phases of electromagnetic waves and a plurality of amplitudes of the electromagnetic waves using a time of flight (ToF) module, inputting the plurality of phases and the plurality of amplitudes into a pre-trained neural network model to output a plurality of phase corrections, adding the plurality of phases and the plurality of phase corrections to output a plurality of corrected phases, and converting the plurality of corrected phases to output a plurality of corrected ranges between the ToF module and surrounding targets.