Predictive Zero-Phase Filtering for Real-Time Signal Separation
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Solution Overview
Problem
Conventional causal filters introduce phase shift, leading to increased strain on batteries in power management systems, as they fail to separate frequency components without delay, necessitating larger battery sizes and reduced battery lifetimes.
Innovation Solution
A noncausal zero-phase filtering algorithm that predicts future signal values and combines them with past measurements to perform real-time filtering without phase delay, using machine learning for prediction and a four-step filtering process involving filtering and time reversal operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional causal filters are used to separate frequency components, then filtering function is achieved, but phase shift is introduced causing delay
Solution Approach 1:
The system performs preliminary action by predicting future signal values using machine learning models before the actual filtering operation. This allows the filter to access future information and compensate for phase delays, achieving zero-phase filtering in real-time applications.
Solution Approach 2:
The patent inverts the traditional causal filtering approach by using noncausal filtering with predicted future values. Instead of filtering based only on past and present values, the system incorporates predicted future signal values to eliminate phase shift, effectively working backwards from the desired zero-phase outcome.
2Device complexity
If phase delay is introduced by causal filters, then filtering is simpler, but battery strain increases and lifetime reduces
Solution Approach 1:
The system performs preliminary prediction of future signal values using machine learning models, allowing the filter to prepare compensation strategies in advance. This preliminary action enables the system to mitigate battery strain by anticipating signal variations and adjusting power management accordingly.
Solution Approach 2:
The patent implements feedback mechanisms where predicted future values are continuously incorporated into the filtering process. This feedback loop allows the system to adjust its filtering and power management strategies in real-time, reducing unnecessary battery cycling and extending battery lifetime.
3Loss of time
If noncausal zero-phase filtering with prediction is used, then phase delay is eliminated, but system complexity increases
Solution Approach 1:
The patent introduces machine learning prediction models as intermediary components between the input signal and the filtering process. These intermediaries generate predicted future values that facilitate zero-phase filtering, managing the complexity by modularizing the prediction and filtering functions.
Solution Approach 2:
The system replaces traditional mechanical or electronic phase-compensation mechanisms with software-based machine learning prediction. This substitution eliminates the need for complex hardware modifications while achieving zero-phase filtering through intelligent algorithms.
4Duration of action of stationary object
If larger battery sizes are used to compensate for phase delay effects, then battery lifetime is maintained, but system cost and size increase
Solution Approach 1:
The system performs preliminary prediction of power management requirements using predicted signal values. This allows the battery to be sized more efficiently by anticipating future power demands and reducing peak-to-average ratios, thereby extending battery lifetime without increasing battery capacity.
Solution Approach 2:
The patent changes the operational parameters of the battery system by implementing zero-phase filtering, which eliminates phase delay-induced strain. This parameter change in the filtering approach reduces the effective stress on the battery, extending its lifetime without requiring larger capacity.
Data Source
AI summary
Systems, apparatuses, methods and computer processes to separate and select frequency components of a signal in real time without phase delay by predicting or forecasting future values of the signal and using that forecast and past measurements in a noncausal zero-phase filtering algorithm are provided. The method of separating and selecting frequency components of a signal in real time without phase delay using a zero-phase filter, comprises obtaining past measurements of the signal; obtaining predicted values of the signal; and using the predicted values of the signal and the past measurements of the signal as components of an input signal in a noncausal zero-phase filtering algorithm for a zero-phase filter, the zero-phase filter producing an output signal.