Synchronic Blind Multichannel Identification and Deconvolution with Kalman Filter

 Generally, blind multichannel identification is the prerequisite step for the blind deconvolution of received signals in the field of channel equalization and speech dereverberation. The blind deconvolution procedure is that of blind multichannel identification followed by signal deconvolution. A novel method, which blind multichannel identification and deconvolution are performed synchronically with Kalman filter, is proposed. The state vector is composed of the multichannel impulse responses and the source signal vector, the process and measurement equations are constructed with the Cross Relation conditions and the Input-Output relation of SIMO system. In addition, the blind multichannel identification and the deconvolution parts can be decoupled to generate two seemingly indenpendent Kalman filters, furthermore, the two Kalman filters can be implemented in parallel. Comparing with cascade structure, the parallel structure can be implemented more efficiently and is beneficial to on-line signal processing. Simulations show that the algorithm works perfectly on ideal signal model (the Signal-to-Error Ratio of the deconvolved signal >100 dB) and it also works well for real-word recorded signals.