Neural ODE State Sensing for Intermittent Continuous-Time Tracking
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Solution Overview
Problem
Current tracking systems for devices with continuous-time dynamics face challenges in effectively integrating past measurements and trajectory history, particularly with intermittent Wi-Fi beam training measurements, leading to impractical state space transformation and imbalanced training data.
Innovation Solution
An AI system utilizing a dynamic autoencoder with neural Ordinary Differential Equations (ODEs) for continuous-time state space transformation, enabling the encoding and decoding of time-series data across different state spaces, and handling imbalanced training data by propagating latent dynamics forward and backward in time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sequence-based approaches are used for trajectory estimation, then integration of past measurements is improved, but reliability deteriorates due to intermittent Wi-Fi beam training measurements
Solution Approach 1:
The patent applies dynamics by transitioning from static frame-based approaches to dynamic sequence-based approaches that adapt to intermittent measurements. The system dynamically adjusts to varying measurement availability by using recurrent neural networks that can handle temporal dependencies and missing data points, making the trajectory estimation reliable despite intermittent Wi-Fi beam training measurements.
Solution Approach 2:
The patent introduces an intermediary mechanism - a recurrent neural network - that mediates between intermittent Wi-Fi measurements and continuous trajectory estimation. This intermediary processes the discontinuous input data and produces smooth, continuous trajectory estimates, resolving the contradiction between integrating past measurements and maintaining reliability.
2Adaptability or versatility
If autoencoder is extended to transform data among different state spaces, then adaptability is improved, but training data balance deteriorates
Solution Approach 1:
The patent applies universality by designing an autoencoder with multiple decoders that can transform latent representations into different state spaces simultaneously. This multi-functional architecture allows the same encoder to serve multiple transformation purposes, improving adaptability while efficiently utilizing available training data across different target spaces.
Solution Approach 2:
The patent applies preliminary action by pre-training the encoder on source domain data before extending to multiple target state spaces. This preliminary encoding capability is then leveraged for multiple transformation tasks, allowing the system to adapt to different state spaces without requiring perfectly balanced training data for each target space.
3Device complexity
If static autoencoder is used for data transformation, then device complexity is reduced, but productivity deteriorates for continuous-time dynamics tracking
Solution Approach 1:
The patent applies continuity by implementing continuous-time dynamics tracking within the autoencoder framework. The recurrent neural network components enable continuous processing of time-series data, maintaining useful action across continuous time intervals rather than discrete frames, thus improving productivity for dynamic tracking applications.
Solution Approach 2:
The patent applies dynamics by transforming the static autoencoder into a dynamic system capable of handling continuous-time variations. The recurrent neural network introduces temporal dynamics that allow the system to track moving objects and changing states efficiently, improving productivity without requiring overly complex architectures.
Data Source
AI summary
A system for sensing a state of a device is provided. The system includes an autoencoder comprising an encoder, a latent subnetwork, and an extended decoder. The encoder encodes each input data point of input data from an input state space into a latent space to produce latent data points and propagates the latent data points with a neural Ordinary Differential Equation (ODE) to estimate an initial point of latent dynamics of the device in the latent space. The latent subnetwork propagates the initial point till a time index of interest using the neural ODE to produce a state of latent dynamics of the device at the time index of interest. The extended decoder decodes the state of latent dynamics of the device into an output state space different from the input state space to produce output data including the state of the device at the time index of interest.