Fixed-Lag Kalman Smoothing for Non-Stationary Signal Denoising
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Solution Overview
Problem
Existing adaptive noise reduction techniques suffer from limited adaptivity and lag error, particularly when dealing with non-stationary physiological signals like photoplethysmographic signals.
Innovation Solution
A system employing a fixed lag Kalman smoother with specific configurations, including setting the state noise covariance matrix equal to measurement noise, the observation matrix equal to the reference signal, and the oblivion coefficient to about one, to adaptively filter reference signals and generate an estimate of the source noise component, thereby reducing lag error and enhancing adaptivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional adaptive filtering techniques (least mean squares, Kalman filters) are used, then noise reduction is achieved, but adaptivity is limited and lag error occurs
Solution Approach 1:
The patent implements a dynamic adaptivity mechanism where the filter continuously adjusts its parameters based on the statistical properties of the input signal. The adaptivity parameter α is dynamically updated based on the correlation between reference and primary inputs, allowing the filter to adapt to non-stationary physiological signals while maintaining low lag error through optimized convergence behavior
2Adaptability or versatility
If adaptive filtering is applied to non-stationary physiological signals, then noise removal is improved, but lag error increases
Solution Approach 1:
The patent employs parameter optimization techniques where the filter coefficients and adaptivity parameters are carefully tuned to achieve the optimal balance between tracking non-stationary signal characteristics and minimizing lag error. The use of normalized update rules and constrained parameter ranges ensures rapid adaptation without excessive time delay
3Measurement precision
If existing Kalman filter approaches are used, then some noise reduction is achieved, but the filter suffers from limited adaptivity
Solution Approach 1:
The patent incorporates feedback mechanisms where the filter continuously monitors the correlation between reference and primary inputs and adjusts its adaptivity parameter accordingly. This feedback loop enables the filter to maintain optimal performance across varying signal conditions by automatically increasing or decreasing adaptivity based on the current signal characteristics and noise levels
Data Source
AI summary
An apparatus includes a sensor module configured for receiving sensed information indicative of a sensed signal. The sensed signal includes a source signal component and a source noise component. The apparatus also includes a reference module configured for reference information indicative of a reference signal. The reference signal also includes a reference noise component. The apparatus also includes a filter module configured as a fixed lag Kalman smoother. The filter module is configured for adaptively filtering the reference signal to generate an estimate of the source noise component. The apparatus also includes a processing module configured for calculating an output signal based on the sensed signal and the estimate of the source noise component. The apparatus also includes an interface module configured for transmitting an indication of the output signal. The filter module is further configured for, based on the output signal, tuning the Kalman smoother.