Signal Vector Derivation Using MUSIC Spectrum Analysis
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Solution Overview
Problem
Existing methods for measuring magnetic fields lack accuracy in determining the direction of signal sources, particularly in environments with multiple signal sources and complex sensor configurations.
Innovation Solution
A signal vector derivation apparatus that uses a spectrum deriving section and a direction deriving section to analyze measurement results from multiple sensors, employing first and second coefficients to derive the direction of triaxial components of the signal vector, allowing for precise localization of signal sources using the MUSIC method and eigenvectors of the noise subspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional subspace methods (MUSIC, SF, WSF) are used for signal source localization, then the measurement of magnetic field can be performed, but the accuracy of determining signal source direction is insufficient
Solution Approach 1:
The patent segments the signal processing by separately deriving the spectrum using measurement results and first coefficients, then deriving direction using second coefficients. This segmentation allows independent optimization of localization accuracy and direction precision without increasing overall system complexity
Solution Approach 2:
The patent introduces two distinct coefficient sets (first coefficients for spectrum derivation, second coefficients for direction derivation) to transform the measurement data. This parameter change enables enhanced measurement precision by optimizing each derivation step with appropriate coefficients rather than using a single complex transformation
2Loss of information
If multiple sensors measuring triaxial components are used, then more signal information can be obtained, but the complexity of data processing increases
Solution Approach 1:
The patent extracts directional information separately from the triaxial measurement data by using second coefficients specifically for direction derivation. This extraction process isolates the direction component from the full measurement data, reducing processing complexity while preserving complete signal information
Solution Approach 2:
The patent transforms the triaxial measurement data into spectral information and directional information through coefficient-based transformations. This dimensionality change converts complex multi-dimensional sensor data into simplified spectral peaks and direction vectors, making processing more manageable while retaining all essential signal characteristics
Data Source
AI summary
A signal vector derivation apparatus receives measurement results from a plurality of sensors that receive signals each represented by a vector having a predetermined direction and measure triaxial components orthogonal to each other and derives the direction of the vector. The measurement results from the sensors are each proportional to a sum of the triaxial components of the vector multiplied, respectively, by first coefficients. The signal vector derivation apparatus includes a spectrum deriving section and a direction deriving section. The spectrum deriving section derives a spectrum obtained based on the measurement results from the sensors and a sum of the first coefficients multiplied, respectively, by second coefficients, the spectrum having local maximum values within voxels in which signal sources that output the respective signals exist. The direction deriving section derives the direction of the vector based on the second coefficients used to obtain the spectrum.