Systems, apparatuses and methods for adaptive noise reduction
Inventors
CENCI, IVAN • TOGNETTI, SIMONE
Assignees
Publication Number
US-10679599-B2
Publication Date
2020-06-09
Expiration Date
2035-04-16
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Abstract
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.
Core Innovation
The invention relates to systems, apparatuses, and methods for adaptive noise reduction using a fixed lag Kalman smoother. The apparatus receives sensed information indicative of a sensed signal that includes both a source signal component and a source noise component. It also receives a reference signal with a reference noise component associated with the source noise component. The core innovation lies in adaptively filtering the reference signal using a fixed lag Kalman smoother configured with specific tuning parameters such as setting the state noise covariance matrix equal to the measurement noise, using the reference signal as the observation matrix, and setting the oblivion coefficient to about one. The apparatus calculates an output signal based on the sensed signal and the estimate of the source noise component, and tunes the Kalman smoother based on the output signal.
The problem being solved arises from the limitations of existing adaptive noise canceling techniques, which have restricted adaptivity and suffer from lag error. Prior art approaches like least mean squares, recursive least squares, and conventional Kalman filters were inadequate in handling signals that are non-stationary or have sharp discontinuities, common in physiological signals such as photoplethysmography. Thus, there is a need for an adaptive noise reduction system that can improve adaptivity and reduce or eliminate lag error, enabling more accurate signal estimation amidst noise and interference.
Claims Coverage
The patent includes multiple independent claims covering both an apparatus and a method with inventive features centered on adaptive filtering using a fixed lag Kalman smoother and its tuning.
Adaptive noise reduction using fixed lag Kalman smoother
An apparatus with a sensor module receiving sensed signals comprising source signal and noise components, a reference module receiving a reference signal with noise correlated to the source noise, and a filter module implemented as a fixed lag Kalman smoother that adaptively filters the reference signal to estimate the source noise by setting the observation data to be the source signal, the observation matrix to be the reference signal, and the oblivion coefficient to about one.
Output signal calculation based on noise estimation
A processing module configured to calculate an output signal associated with the source signal component by using the sensed signal and the estimated source noise component, including subtracting the estimated noise from the sensed signal to generate the output.
Adaptive tuning of the Kalman smoother parameters
The filter module is configured to tune the Kalman smoother parameters based on the output signal by modifying the state noise covariance, oblivion coefficient, and order (length) of the Kalman smoother. State noise covariance tuning involves incrementing or decrementing based on whether the sensed or reference signals exceed respective thresholds, with user input optionally modifying the thresholds and increments.
Use of physiological sensors
Inclusion of a photoplethysmographic sensor for capturing sensed information and an accelerometer sensor for capturing reference information to assist in adaptive noise reduction in physiological signals.
Database integration for sensed and reference information
Integration of a database and corresponding database module within the apparatus to store and retrieve sensed and reference information for processing by the sensor and reference modules.
The independent claims cover an apparatus and a method for adaptive noise reduction utilizing a tuned fixed lag Kalman smoother to estimate and remove source noise components from sensed signals, particularly focusing on physiological sensing applications, and enabling parameter tuning to improve adaptivity and reduce lag error.
Stated Advantages
Enhanced adaptivity in noise reduction for signals that are deterministic or stochastic, stationary or time variable.
Reduction or elimination of lag error inherent in prior adaptive filtering approaches through the use of a fixed lag Kalman smoother with tunable parameters.
Ability to adaptively tune key filter parameters such as state noise covariance, oblivion coefficient, and filter order based on the output signal for improved responsiveness.
Applicability to physiological signals with non-stationary characteristics and sharp spikes, such as photoplethysmographic signals, enabling more accurate signal estimation.
Documented Applications
Removing motion artifacts from photoplethysmographic signals for improved physiological monitoring.
Electrocardiography applications including removal of 60 Hz noise, donor ECG in heart transplants, and maternal ECG to estimate fetal heart signals.
Speech processing to remove ambient noise from speech signals.
Antenna design to eliminate sidelobe interference.
General removal of periodic interference such as background hum in music recordings.
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