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In this study, the authors propose a novel full-diversity combination algorithm to significantly improve the performance of the network Kalman-based blind equalizers. Based on the weighted Gaussian sum (WGS) technique and the network of extended Kalman filters (NEKF), the proposed full-diversity blind equalizer can employ the prediction errors of network of Kalman filters to achieve the maximum likelihood (ML) detection. In the first initial condition, the proposed full-diversity blind equalizer requires an initial training sequence in order to estimate the initial channel coefficients. For symbol detection, the proposed full-diversity blind equalizer demonstrates a significant improvement over the conventional WGS-IMM(Interacting Multiple Model) blind equalizer in the bit error rate (BER) performance. Besides, from the trade-off between performance and computational complexity, the proposed modified 2-Diversity blind equalizer is shown to be a best choice for the WGS-based blind equalizer.