Blind Sensor Array Calibration via Iterative Gain Estimation
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Solution Overview
Problem
Current blind calibration methods for sensor arrays in radio interferometry, magnetic resonance imaging, and ultrasound imaging face challenges such as high computational complexity, especially in low signal-to-noise ratio environments, and are sensitive to the accuracy of strong source data, which can lead to performance losses when dealing with weak sources.
Innovation Solution
A computer-implemented method that iteratively estimates source intensity and sensor gain amplitude and phase using beamforming matrices, assuming fixed gains, and employs message passing algorithms in bipartite factor graphs to reduce computational complexity and improve convergence, allowing for calibration of sensor arrays with reduced computational efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blind calibration methods use convex optimization or message passing algorithms to handle low signal-to-noise ratio environments, then calibration can be performed without known sources, but computational complexity increases significantly
Solution Approach 1:
The patent segments the calibration problem into two distinct phases: (1) beamforming with initial gain assumptions to obtain source intensity estimates, and (2) gain estimation using those intensity estimates. This segmentation allows the complex joint estimation problem to be decomposed into more manageable steps, reducing overall computational complexity while maintaining calibration accuracy in low SNR environments.
Solution Approach 2:
The patent performs preliminary beamforming operations with fixed gain assumptions before conducting the actual gain estimation. This preliminary action generates source intensity estimates that serve as input for the subsequent gain estimation step, enabling the system to proceed with calibration even when direct gain estimation from raw measurements would be unreliable due to low SNR.
2Productivity
If supervised calibration methods use strong sources for estimation, then calibration can be performed with available data, but weak sources are disregarded leading to performance loss
Solution Approach 1:
The patent implements a self-service calibration approach where the system uses its own beamformed measurements and intensity estimates to automatically determine sensor gains without relying on external strong source information. This self-service mechanism allows the calibration to incorporate data from all sources including weak ones, improving overall calibration performance while maintaining efficiency.
3Ease of operation
If beamforming is performed with fixed gain assumptions, then computational operations can be simplified, but accurate gain estimation requires additional iterative steps
Solution Approach 1:
The patent implements a feedback loop where initial beamforming with fixed gains produces intensity estimates, which then feed into gain estimation, and the updated gains are fed back into the beamforming process. This feedback mechanism allows the system to start with simple fixed-gain beamforming operations while systematically improving accuracy through iterative refinement, balancing computational simplicity with estimation accuracy.
Data Source
AI summary
Embodiments include methods for calibrating sensors of one or more sensor arrays. Aspects include accessing one or more beamforming matrices respectively associated to the one or more sensor arrays. Source intensity estimates are obtained for a set of points in a region of interest, based on measurement values as obtained after beamforming signals from the one or more sensor arrays based on the one or more beamforming matrices, assuming fixed amplitude and phase of gains of sensors of the one or more sensor arrays. Estimates of amplitude and phase of the sensor gains are obtained based on: measurement values as obtained before beamforming; and the previously obtained source intensity estimates. The obtained estimates of amplitude and phase can be used for calibrating sensors.