The original form of the turbo equalizer is based on two maximum a posteriori probability (MAP) procedures. The MAP-based turbo equalizer using trellis diagram can significantly improve the bit error rate (BER) performance while it suffers from high computational complexity. Therefore, we propose a new suboptimal iterative turbo equalizer based on Kalman filtering framework in order to reduce high complexity of the MAP-based technique. The proposed receiver produces a posteriori information for a priori information of the channel decoder in the filtering step and obtains a priori information from the extrinsic information of the channel decoder. The proposed Kalman-based turbo equalizer operates in exactly the same way as the conventional Kalman equalizer. In the prediction step, we use the soft-valued extrinsic information of the channel decoder as a priori information of the desired signal to estimate and in the filtering step, calculate a posteriori information of the desired signal to estimate, which is a newly updated conditional probability when the a priori probability is given in the prediction step.