Abstract:Aiming at the problem that the Gerschgorin disk estimator (GDE) and its improved algorithm cannot accurately calculate the number of changing instantaneous signal sources, an improved GDE method based on the combination of sliding window (SW) and correlation coefficient (CC) is proposed, which is called GDESWCC dynamic source number blind estimation method. Firstly, using the characteristic that the Gerschgorin disk radius of the GDE changes constantly in the increasing process of the number of signals, the dynamic segment with the largest radius change in the whole source is obtained by subtracting the old radius from the new Gerschgorin Disk radius. Secondly, the sliding window algorithm is used to precisely estimate the dynamic segment, and the judgment threshold of GDE for each sliding window is obtained. Then, the judgment threshold of GDE is taken as the characteristic quantity of the sliding window, and the correlation coefficients among them are calculated. According to the peak position of the correlation coefficients, the dynamic window signal disk and the static window signal disk are distinguished to obtain the number of instantaneous signal sources. Finally, computer simulation and actual experiment data verify the effectiveness, universality and practicability of the proposed algorithm. The comparison of computer simulation and experiment results shows that compared with the existing static GDE, the proposed algorithm can quickly interpret the number of signal sources and locate the dynamic changing time region. On this basis, the simulation experiment on dynamic source signal number estimation in the case of underdetermined blind source separation was carried out with the proposed algorithm combining ensemble empirical mode decomposition (EEMD). The results show that the correct estimation can be obtained as long as the adjustment factor is greater than 02. The actually measured data in experiment are basically consistent with the simulation results. Especially when the number of signal sources decreases, the estimated correct probability of GDE decreases from 95% to 4%, while the estimated correct probability for the proposed algorithm in this paper increases from 95% to 97%.