Signal Source Positioning Using Triaxial Sensors and L1 Regularization
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Solution Overview
Problem
Existing methods for estimating the positions of multiple signal sources, such as the LORETA, MUSIC, and Lasso methods, face challenges in accurately determining the positions of coherent signal sources and multiple signal sources due to blurry estimations and poor accuracy.
Innovation Solution
A signal source specifying apparatus that utilizes a relational matrix to record the relationship between measurement results from multiple sensors and signal vectors. This apparatus derives the positions and vectors of signal sources by minimizing a cost function that includes an error function and a normalization term, improving the accuracy of estimating multiple signal sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If the LORETA method is used to estimate signal source positions, then estimation can reach deeper positions, but the estimation becomes blurry and cannot distinguish multiple signal sources
Solution Approach 1:
The patent segments the signal source estimation problem by introducing a sparsity constraint that separates active signal sources from inactive regions. The L1 norm regularization divides the continuous space into discrete active and inactive zones, enabling clear separation of multiple signal sources while maintaining deep estimation capability through the underlying forward model.
2Measurement precision
If the MUSIC method is used to estimate multiple signal source positions, then multiple sources can be identified, but coherent signal sources (same frequency and phase) cannot be estimated
Solution Approach 1:
The patent changes the mathematical parameters by using L1 norm regularization instead of the conventional L2 norm or eigenvalue decomposition. This parameter change in the optimization approach enables the method to handle coherent signal sources by promoting sparse solutions that can distinguish between correlated signals from different locations, overcoming the fundamental limitation of subspace methods like MUSIC.
3Device complexity
If the Lasso method is used with single-axis magnetic sensors, then computation is simplified, but accuracy in estimating multiple signal sources deteriorates
Solution Approach 1:
The patent adds dimensional information by incorporating triaxial sensor measurements (three orthogonal components) instead of single-axis measurements. This dimensional enhancement provides sufficient information to resolve the ambiguity in estimating multiple signal sources, particularly coherent sources, while the L1 norm constraint maintains computational tractability similar to the original Lasso method.
Data Source
AI summary
A signal source specifying apparatus receives measurement results from a plurality of sensors that receive, from a plurality of signal sources, signals represented by vectors each having a predetermined direction and measure triaxial components orthogonal to each other to specify positions of the signal sources and the vectors. The signal source specifying apparatus includes a relational matrix recording section, and a position/vector deriving section. The relational matrix recording section records a relational matrix representing a relationship between the measurement results summarized per axis by a number of the sensors and the vectors. The position/vector deriving section derives the positions of the signal sources and the vectors that offer a minimum cost function based on the measurement results and the relational matrix. The positions of the signal sources and the vectors are specified based on a result of derivation by the position/vector deriving section.