Improved spatial smoothing method for generating an autocorrelation matrix of radio signal measurement values
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Solution Overview
Problem
Existing methods for generating an autocorrelation matrix to determine signal propagation properties are inefficient in terms of computing time and accuracy, particularly when using spatial smoothing techniques, leading to excessive rounding errors.
Innovation Solution
A method for forming an autocorrelation matrix with a smaller size by selecting non-adjacent measurement value vectors to create subspace matrices, which are then correlated and added, allowing for flexible choice of computing time and accuracy, with optional weighting based on signal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spatial smoothing methods are used to generate autocorrelation matrix, then measurement value space can be reduced, but computing time increases and rounding errors occur
Solution Approach 1:
The patent extracts only the necessary subset of measurement value vectors to form subspace matrices, rather than using all available vectors. By selecting specific vectors (e.g., every second, third, or nth vector) to form subspace matrices of size g×a where g<f, the method reduces the number of correlations and additions required, thereby reducing computing time while maintaining sufficient accuracy for signal propagation determination.
Solution Approach 2:
The patent segments the full set of f measurement value vectors into multiple subspace matrices, each formed from a selected subset of vectors. This segmentation allows parallel or sequential processing of smaller matrix operations instead of one large operation, reducing overall computing time and rounding errors while preserving the essential information needed for accurate signal propagation analysis.
2Productivity
If smaller autocorrelation matrix is formed to reduce computing time, then computation is faster, but accuracy of derived data may decrease
Solution Approach 1:
The patent changes the parameter of subspace matrix size from the traditional full size to a reduced size g×a where g<f. By carefully selecting the reduction factor and the specific vectors included in each subspace matrix, the method achieves optimal balance between computing speed and accuracy. The patent demonstrates that even with reduced matrix size, sufficient accuracy is maintained for determining signal propagation properties.
3Measurement precision
If more subspace matrices are used to improve accuracy, then data accuracy increases, but computing time increases excessively
Solution Approach 1:
The patent applies partial action by forming only the necessary number of subspace matrices required to achieve sufficient accuracy, rather than using all possible combinations. The method determines an optimal number of subspace matrices that provides adequate accuracy for signal propagation determination without excessive computing overhead, achieving the right balance between accuracy and efficiency.
Data Source
AI summary
A method for providing an autocorrelation matrix of measurement values of wireless signals between a first and a second object for determining at least one property of the signal propagation of the wireless signals between the first and the second object having f, frequency measurement value vectors, each frequency measurement value vectors having a coordinates with a>=1 and with f>1 which are provided. An autocorrelation matrix with a frequency vector number smaller than f is formed from the set of frequency measurement value vectors by means of spatial smoothing for performing spatial smoothing, a plurality of subspace matrices each having a number of frequency measurement value vectors are formed and wherein the subspace matrices are each correlated with themselves and the subspace matrices correlated with themselves are added up.