To reduce channel noise,fading,and inter-user interference effectively in the chaotic communication systems with multi-user,a blind channel equalization algorithm based on dual unscented Kalman filter algorithm is proposed.Assuming that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model,two separate state-space representations are used for the signals and coefficients.Then two unscented Kalman filters are used to estimate chaotic signals and channel coefficients simultaneously.The simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed. To reduce channel noise, fading, and inter-user interference effectively in the chaotic communication systems with multi-user, a blind channel equalization algorithm based on dual unscented Kalman filter algorithm is proposed. Assuming that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model, two separate state-space representations are used for the signals and coefficients.Then two unscented Kalman filters are used to estimate chaotic signals and channel coefficients simultaneously. The simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed.