Microbeamforming With Error Compensation

One of the main issues in the development of 2-D arrays is the high system complexity due to the requirement for a large number of elements. The 2-D array systems suffer from high system complexity. The microbeamforming (MBF) method has been proposed to reduce the system complexity; however, distortions of MBF approach such as focusing errors of postbeamforming process result in broadening the main lobe and increasing the sidelobe and grating-lobe levels, which together degrade the image quality. As the presteered radio frequency (RF) data can be estimated from MBF data at the digital back end, better postbeamforming can be performed and higher image quality can be achieved. In this paper, a compensation approach is proposed to estimate the presteered RF data from MBF data by utilizing additional headers and compensation factors. The compensation factors and headers are estimated at the probe front end and then applied to the back-end digital system to reconstruct the required presteered RF data. As the absolute values of the MBF errors are modeled as a single-sided Gaussian distribution, the theoretical mean square error with the proposed method is approximately 2.75 times lower than that of its counterpart without compensation; this implies better reconstruction of presteered RF data can be achieved with the proposed method. The simulation results showed that the main lobe is improved, and the sidelobe and grating-lobe levels in both the lateral and elevation directions were improved by 11.73 and 19.12 dB, respectively, while the peak signal-to-noise ratios improved by 6-9 dB with the proposed method. The contrast-to-noise ratios also are enhanced by 0.5 dB when using the proposed method. Analog circuits are presented to demonstrate that this novel compensation method can be realized in practice. The reduction of cables and analog-to-digital converters are about seven-fold compared to fully sampled 2-D array systems as 4 by 4 channels are grouped for the proposed method as well.