Source Signal Separation Using XCSPE for Real-Time Peak Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional signal separation techniques require prohibitive processing power and are often incapable of real or near real-time identification and isolation of signal sources, limiting their effectiveness in separating and enhancing individual signal components.
Innovation Solution
The method employs a source-agnostic approach using Complex Spectral Phase Evolution (CSPE) and Singlet Transform Process to achieve high-resolution signal processing, enabling accurate detection and separation of oscillator peaks from a signal, which are then grouped and reconstituted into coherent groups for enhanced signal separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood estimation (MLE) is used for source signal separation, then separation accuracy can be improved, but the system requires accurate knowledge of sensor positions and orientations which are difficult to obtain in practice
Solution Approach 1:
The patent extracts and eliminates the requirement for accurate sensor position and orientation knowledge from the MLE process. By formulating an objective function that operates directly on the observed mixed signals without requiring explicit sensor geometry parameters, the method removes this complex measurement requirement while maintaining separation accuracy.
Solution Approach 2:
The patent changes the parameter space of the separation algorithm by replacing physical sensor parameters (positions and orientations) with statistical parameters derived from the signal data itself. This transformation allows the system to operate in a parameter-free regime regarding sensor geometry, achieving the same separation goal through different mathematical parameters.
2Reliability
If conventional MLE methods are used, then theoretical separation performance can be achieved, but path propagation effects between sources and sensors are not accounted for leading to degraded practical performance
Solution Approach 1:
The patent applies preliminary anti-action by pre-compensating for path propagation effects in the objective function formulation. By incorporating models of acoustic propagation, electromagnetic wave propagation, or other relevant physics into the separation criterion, the method counteracts the degrading effects before they corrupt the separation results.
Solution Approach 2:
The patent converts the harmful path propagation effects into beneficial information by using the known propagation characteristics to inform the separation process. Rather than treating propagation effects as mere disturbances, the method utilizes them as additional constraints or prior knowledge that actually improves separation accuracy when properly incorporated into the objective function.
3Measurement precision
If accurate sensor position and orientation measurements are obtained using additional sensors, then separation accuracy improves, but the number of sensors and system complexity increase
Solution Approach 1:
The patent extracts the need for additional sensing hardware by reformulating the separation problem to eliminate dependence on precise geometric measurements. The method achieves the same informational goal using only the signal data already captured by the primary sensor array, without requiring extra sensors for calibration or geometry measurement.
Solution Approach 2:
The patent enables the sensor array to serve itself by using the mixed signal data to implicitly capture and exploit the geometric relationships between sensors and sources. The system performs its own geometric characterization through the statistical properties of the observed signals, eliminating the need for external measurement systems or additional sensors.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method of processing a signal includes taking a signal recorded by a plurality of signal recorders, applying at least one super-resolution technique to the signal to produce an oscillator peak representation of the signal comprising a plurality of frequency components for a plurality of oscillator peaks, computing at least one Cross Channel Complex Spectral Phase Evolution (XCSPE) attribute for the signal to produce a measure of a spatial evolution of the plurality of oscillator peaks between the signal, identifying a known predicted XCSPE curve (PXC) trace corresponding to the frequency components and at least one XCSPE attribute of the plurality of oscillator peaks and utilizing the identified PXC trace to determine a spatial attribute corresponding to an origin of the signal.