Based on particle filter theory

a new novel blind identification and equalization algorithm was proposed sam-pling of the posterior probability distribution was avoided by sampling the expectation instead of actual values of the channel

so as to reduce the complexity of algorithm.In addition

the importance sampling process could be simplified greatly by choosing the prior density as the importance function.Simulation results show that the algorithm has fast conver-gence performance and be effective even under the circumstances of very low signal-to-noise ratio and less observation data.