In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is\nproposed for a parallely distributed adaptive signal processing (PDASP) operation.The proposed architecture runs computationally\nexpensive procedures like complex adaptive recursive least square (RLS) algorithmcooperatively.The proposedPDASP architecture\noperates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the\napplication of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP\nscheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is\nobserved that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm\nas well as Kalman filter.Moreover, the proposed architecture provides an improvement of 95.83%and 82.29% decreased processing\ntime parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively.\nLikewise, for high doppler rate, the proposed architecture entails an improvement of 94.12%and 77.28
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