Abstract: A modified distributed particle filtering algorithm based on consensus optimization for cooperative blind equalization is proposed in cooperative receiver networks. In the proposed method, multiple receivers composed of distributed network with no fusion center estimate the transmitted sequences cooperative by using the distributed particle filtering, which improves the bit err ratio performance compared to single receiver affected by fading channel more seriously. In order to guarantee all nodes have the same set of particles and weights, the consensus optimization based on alternating-direction method of multipliers is introduced to evaluate the global likelihood function across the receiver network. Then the maximum consensus iterations are used so that the same importance function and corresponding importance weights for all particles can be same at all receivers. Theoretical analysis and simulation results show that only a few consensus iterations suffice for the proposed algorithm to approach the performance of their centralized counterparts. The fully distributed cooperative scheme achieves spatial diversity gain and decreases the bit error ratio.