Particle filtering algorithms for single-channel blind separation of convolutionally coded signals

In this paper, we propose a particle filtering based algorithm for single-channel blind separation (SCBS) of two convolutionally coded signals. We utilize the convolution code to enhance the performance of separation. By modifying the generalized state-space representation of the SCBS model, we obtain an approach for joint separation and decoding of two source signals. Two conventional algorithms, which execute decoding after signal separation, are then discussed and compared with the proposed algorithm. Computer simulations show that the proposed algorithm makes a significant improvement in symbol error rate (SER) performance over the two conventional algorithms.