Performance of maximum-likelihood deconvolution for Bernoulli-Gaussian processes
Chong‐Yung Chi · 2003
A performance analysis is proposed for Bernoulli-Gaussian processes distorted by a linear time invariant system with regard to false alarms, correct detections, and the resolution for the well-known maximum-likelihood deconvolution (MLD). The analysis led to six main conclusions with regard to the dependence of the performance of the single-most-likely-replacement algorithm upon both SNR and the mainlobe width of the normalized autocorrelation function gamma (k) of nu (k): the performance is better for larger SNR and gamma (k) with a narrower mainlobe; the performance is not dependent upon the wavelet length; the performance can be infinitely improved by increasing SNR no matter whether the mainlobe of gamma (k) is broad or narrow; although false alarms for gamma (k) with a broad mainlobe cannot be removed by increasing SNR, their amplitudes tend to be smaller for larger SNR; for the same performance, a higher SNR is required for gamma (k) with a broad mainlobe than for gamma (k) with a narrow mainlobe; and the resolution is better for gamma (k) with a narrower mainlobe. It is believed that these conclusions should apply to other comparable suboptimal ML algorithms and that this analysis can help users explain the deconvolved data from the viewpoints of both SNR and gamma (k). Simulation results using synthetic data which support the proposed analysis are presented.>