Super-resolution Time-of-Arrival Estimation via Sub-band Signal Segmentation
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Solution Overview
Problem
In wireless communication systems, determining the time of arrival of wireless signals is challenging due to multipath interference, especially when there is insufficient data to uniquely determine the covariance matrix, leading to underdetermined conditions and degraded communication performance.
Innovation Solution
An electronic device with an interface circuit that receives wireless signal samples, splits them into parallel channels for sub-band processing, including filtering and decimation, and applies super-resolution techniques such as MUSIC or linear-prediction to estimate the time of arrival and improve signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If covariance matrix based high-resolution techniques are used to identify line-of-sight signals, then measurement precision is improved, but device complexity increases due to difficulty in determining the covariance matrix
Solution Approach 1:
The patent segments the signal processing into distinct functional blocks: received signal input, covariance matrix calculation, eigenvalue decomposition, and time of arrival selection. This segmentation makes the complex process more manageable and implementable in practical systems while maintaining measurement precision.
2Measurement precision
If multiple instances or repetitions of wireless signals are acquired to determine the covariance matrix uniquely, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary signal processing and covariance matrix calculation on available signal instances before final time of arrival estimation. By preparing the covariance matrix in advance from available data, the system avoids the need for additional repeated measurements in time-sensitive applications.
3Measurement precision
If sub-band or super-resolution techniques are used to estimate minimum time-of-arrival, then measurement precision is improved, but device complexity increases due to computational requirements of matrix operations
Solution Approach 1:
The patent segments the large-scale matrix operations into smaller sub-band processing tasks. By dividing the frequency spectrum into multiple sub-bands and processing each separately, the computational complexity of eigenvalue decomposition is reduced while maintaining super-resolution estimation precision.
Solution Approach 2:
The patent transforms the problem from processing the full bandwidth signal at once to processing multiple narrower sub-band signals. This dimensional transformation reduces the computational burden of matrix operations while achieving the same time of arrival estimation precision through spectral decomposition.
Data Source
AI summary
An interface circuit in an electronic device may receive samples of wireless signals in one or more time intervals, where the wireless signals are associated with the second electronic device. For example, the samples of the wireless signals may include one or more of: time samples, spatial samples and/or frequency samples. Then, the interface circuit may split the samples of the wireless signals into parallel channels for sub-band processing that improves a resolution and/or a signal-to-noise ratio of the samples of the wireless signals. The sub-band processing may include filtering and decimation. Moreover, the sub-band processing may include a super-resolution technique, such as a multiple signal classification (MUSIC) technique and/or a linear-prediction super-resolution technique. Next, the interface circuit may combine outputs from the parallel channels to estimate a time of arrival of the wireless signals and/or a distance between the electronic device and the second electronic device.