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The primary impediment to multi-channel system identification appears in many applicationareas such as acoustics and mobile communication. In this thesis, primary methods are presented, which is based on the normalised least-mean-squares (NLMS) algorithm in combinationwith a particular state of excitation signals called perfect sequences (PSEQs).Those are peri-odically reproduced a pseudo-noise signal. Based on the periodic excitation signal the NLMSprocess can identify a noiseless linear signal inside one period.In this project, I explained an approach to generalise the fundamental concept of system identification to a multi-channel system from cite{lite5}.It uniquely identifies the impulse responses of multiple channels with one measurement for all numbers of multi-channel and all system lengths.Also, the method allows an identification of each kind of radio and can easily be elongated to multiple inputs – multiple outputs (MIMO).The system identification methods in MIMO systems used in wireless transmission as well as calibration of multi loudspeakers system in evaluation of echo cancellation process cite{lite2}.The fundamental question is that how to build the excitation signals to identify the various paths of a single inputs – multiple output (SIMO) system.Incite{lite5} they developed a procedure how to conclude the concept of multi-channel system identification MISO system.The standard obstacles of multi-channel identification as required in sound simulation in multi-channel been seen as the distinction of non-zero correlation and the excitation signal.This method is extended to any kind of acoustic system e.g., MIMO system identification.The adaptive filter does not converge properly and performance of speed convergence is very low.In this project, we are using multi loudspeakers and multi microphones on a circle, The system gives multi measured source signal, and adequate excitation signals are required to recognize the real impulse response of each path.In this project, I proposed a dynamic measurement approach used fromcite{lite3} to captivate a substantial number of impulse responses in short period. In pre-processing, the moving microphone captures the response of an acoustic system continuously, and in the post-processing, it estimates the immediate impulse responses. we originated the system identification method to counterbalanced time-varying in the continuous moving microphone, perhaps the captured impulses were rendered as the orthogonal sequence coefficients of the instant responses. I implemented this method on the moving circular path, where the responses are estimated from interpolated orthogonal coefficients.The association of impulse length, the speed of the moving microphone along with measurements are explained inheritance to the bandwidth of the spatial responses.Based on the current techniques, the measurements from impulses are so-called dynamic measurement.In which the outcomes were correlated with static measurements.

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