TAILIEUCHUNG - Báo cáo hóa học: " Clipped Input RLS Applied to Vehicle Tracking Hadi Sadoghi Yazdi"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Clipped Input RLS Applied to Vehicle Tracking Hadi Sadoghi Yazdi | EURASIP Journal on Applied Signal Processing 2005 8 1221-1228 2005 Hindawi Publishing Corporation Clipped Input RLS Applied to Vehicle Tracking Hadi Sadoghi Yazdi Department of Electrical Engineering Tarbiat Modarres University . Box 14115-143 Tehran Iran Email sadoghEy@ Mojtaba Lotfizad Department of Electrical Engineering Tarbiat Modarres University . Box 14115-143 Tehran Iran Email lotfizad@ Ehsanollah Kabir Department of Electrical Engineering Tarbiat Modarres University . Box 14115-143 Tehran Iran Email kabir@ Mahmood Fathy Faculty of Computer Engineering Iran University of Science and Technology Tehran 16844 Iran Email mahfathy@ Received 24 July 2004 Revised 27 November 2004 Recommended for Publication by John Homer A new variation to the RLS algorithm is presented. In the clipped RLS algorithm CRLS proposed in updating the filter weights and computation of the inverse correlation matrix the input signal is quantized into three levels. The convergence of the CRLS algorithm to the optimum Wiener weights is proved. The computational complexity and signal estimation error is lower than that of the RLS algorithm. The CRLS algorithm is used in the estimation of a noisy chirp signal and in vehicles tracking. Simulation results in chirp signal detection shows that this algorithm yields considerable error reduction and less computation time in comparison to the conventional RLS algorithm. In the presence of strong noise also using the proposed algorithm in tracking of 59 vehicles shows an average of reduction in prediction error variance relative to conventional RLS algorithm. Keywords and phrases RLS clipped input data noise cancellation vehicle tracking. 1. INTRODUCTION The subject of adaptive signal processing has been one of the fastest growing fields of research in recent years. The recursive least square RLS and the least mean square LMS are two adaptive filtering algorithms 1 . The adaptive RLS and LMS

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