Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming

Che Chou Shen*, Yong Qi Xing, Gency Jeng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background Generalized coherence factor (GCF) can be adaptively estimated from channel data to suppress sidelobe artifacts. Conventionally, Fast Fourier Transform (FFT) is utilized to calculate the full channel spectrum and suffers from high computation load. In this work, autocorrelation (AR)-based algorithm is utilized to provide the spectral parameters of channel data for GCF estimation with reduced complexity. Methods Autocorrelation relies on the phase difference among neighboring channel pairs to estimate the mean frequency and bandwidth of channel spectrum. Based on these two parameters, the spectral power within the defined range of main lobe direction can be analytically computed from a pseudo spectrum with the presumed shape as the GCF weighting value. A bandwidth factor Q can be further included in the formulation of pseudo channel spectrum to optimize the performance. Results While the GCF computation complexity of a N-channel system reduces from O(Nlog2N) with FFT to O(N) with AR, the lateral side-lobe level is effectively suppressed in the GCF-AR method. In B-mode speckle imaging, the GCF-AR method can provide a higher image contrast together with a relatively low speckle variation. The resultant Contrast-to-Noise Ratio (CNR) improves from 6.7 with GCF-FFT method to 9.0 with GCF-AR method. Conclusion GCF-AR method reduces the computation complexity of adaptive imaging while providing superior image quality. GCF-AR method is more resistant to the speckle black-region artifacts near strong reflectors and thus improves the overall image contrast.

Original languageEnglish
Pages (from-to)177-183
Number of pages7
StatePublished - 1 Dec 2016


  • Adaptive imaging
  • Autocorrelation
  • Computational complexity
  • Generalized coherence factor

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