Multipath Signal Decomposition via Adaptive Basis Vectors
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Solution Overview
Problem
In multipath channel environments where multiple paths are clustered, existing methods struggle to accurately estimate channel parameters due to reduced orthogonality between paths, leading to increased noise sensitivity and estimation errors.
Innovation Solution
A multipath signal decomposition method that acquires observation data, determines basis vectors maximizing projection to add to a dictionary, calculates residuals, and updates the dictionary to minimize the number of basis vectors required for sparse representation, thereby reducing estimation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the Matching Pursuit (MP) algorithm is used to decompose multipath signals, then real-time implementation is achieved, but performance in decomposing clustered multipath components is lowered
Solution Approach 1:
The patent applies dynamic threshold adjustment based on signal characteristics. The threshold for identifying clustered components is not fixed but adapts according to the signal's temporal and spectral properties, allowing the algorithm to dynamically switch between treating components as clustered or separated based on actual signal conditions, thereby maintaining both speed and accuracy
Solution Approach 2:
The patent changes key parameters including the threshold for component separation, the weighting factors in the cost function, and the iteration limits based on the specific characteristics of the multipath signal. By adjusting these parameters adaptively, the algorithm optimizes its performance for different channel conditions while maintaining real-time capability
2Measurement precision
If the Space-Alternating Generating Expectation Maximization (SAGE) algorithm is used to estimate multipath parameters, then estimation accuracy is improved, but computation amount becomes very large
Solution Approach 1:
The patent extracts and removes clustered components from the signal processing pipeline by identifying them through threshold-based detection and handling them separately. This extraction allows the main SAGE algorithm to focus only on the non-clustered components, significantly reducing the computation amount while maintaining accuracy for the remaining components
Solution Approach 2:
The patent segments the multipath signal decomposition process into distinct stages: (1) identification and extraction of clustered components using threshold-based methods, (2) separate processing of extracted components, and (3) reconstruction of the complete signal. This segmentation divides the complex computation into manageable parts, reducing overall computational burden while maintaining estimation accuracy
3Quantity of substance
If basis vectors are added to maximize projection in the dictionary, then sparse representation is achieved with minimum number of basis vectors, but the process becomes computationally intensive
Solution Approach 1:
The patent applies partial action by adding basis vectors only when necessary and using a threshold-based criterion to determine when to stop adding vectors. Instead of exhaustively searching for the optimal sparse representation, the algorithm adds basis vectors partially based on projection thresholds, achieving a balance between sparsity and computational complexity
Data Source
AI summary
The present disclosure provides a multipath signal decomposition method enabling to estimate paths and path parameters. The method includes: (a) acquiring observation data including a plurality of multipath signal components; (b) for each multipath signal component, determining one or more basis vectors maximizing a projection; (c) calculating a residual using basis vectors having an orthogonality higher than a predetermined reference level with a new basis vector and performing a sub-optimization by updating a basis vector maximizing a projection of the residual among the basis vectors having the orthogonality lower than the reference level; (d) unless a termination condition is satisfied, repeating the operations (b) and (c) to determine an additional basis vector and determine basis vectors of a minimum number expressing a corresponding multipath signal component while avoiding a redundancy of the basis vectors; and (e) when the termination condition is satisfied, determining coefficients of basis vectors and parameters.