Ultra-Wideband Sensing With Eigenvalue Separation for Multiple Targets
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Solution Overview
Problem
Conventional UWB sensing systems struggle to accurately detect and locate multiple moving targets due to overlapping reflections, which complicates the distinction between different targets and results in inaccurate distance and angle calculations.
Innovation Solution
The proposed method employs eigenvalue decomposition of covariance matrices derived from channel impulse response estimates to separate the contributions of individual targets, determining eigenvectors corresponding to the largest eigenvalues to estimate the number and propagation delays of targets, and using cross-covariance matrices for angle determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional UWB sensing systems use channel impulse response estimates to detect targets, then the system can obtain propagation channel measures, but multiple targets' reflections overlap and cannot be distinguished
Solution Approach 1:
The patent segments the mixed reflection signals from multiple targets by constructing a covariance matrix from channel impulse response estimates and performing eigenvalue decomposition. This separates the signal space into distinct components corresponding to individual targets, allowing each target's reflection to be analyzed independently despite overlapping in time domain.
Solution Approach 2:
The patent introduces covariance matrix and eigenvalue decomposition as intermediary mathematical tools between the raw channel impulse response and target detection. These intermediaries transform the overlapping reflection signals into separable signal subspaces, enabling distinction between multiple targets that were indistinguishable in the original domain.
2Ease of operation
If algorithms only consider variance evolution of taps, then the processing is simple, but multiple targets cannot be identified
Solution Approach 1:
The patent changes the analysis parameter from simple tap variance evolution to eigenvalues and eigenvectors of the covariance matrix. This parameter transformation enables the system to resolve multiple targets by identifying the number of significant eigenvalues and extracting target information from corresponding eigenvectors, providing both multi-target detection capability and distance estimation.
Data Source
AI summary
Ultra-wideband sensing systems, methods, and devices for multiple-target detection are disclosed. In an exemplary aspect, a method of sensing operation performed by an UWB device is disclosed. The method of sensing operation includes determining a first matrix based on one or more estimated channel impulse responses. The method may also include determining a first set of eigenvectors and a first set of eigenvalues based on the first matrix. The method may also include determining a second set of eigenvectors by identifying which of the first set of eigenvectors corresponds to a number of the largest of the first set of eigenvalues, where the number is equal to an estimated number of targets; and determining a propagation delay value for each of the estimated number of targets based on the second set of eigenvectors.