Fast convergence rate adaptive blind estimation method for characteristic parameter

Abstract

This invention provides a character parameter adaptive blind estimation method with quick convergency speed, which applies a time-varying step to improve the convergency speed of the algorithm and gets the best filter weight coefficient, applies soft judgement weighing to maintain the Rodust of hard judgement errors and channel noises, which is a channel blind estimation method with quicker convergency speed and higher accuracy than the current recursion least square technology and is used in all kinds of communication systems applying OFDM, and in CDMA and TDMS systems, at the same time, such method can be used in all RLS algorithms and its derived algorithms and devices to estimate other character parameters.

Description

Technical field: [0001] The invention relates to a characteristic parameter self-adaptive blind estimation method with fast convergence speed. In particular, it relates to a wireless mobile channel self-adaptive self-estimation method, which belongs to the field of digital mobile communication using electromagnetic wave technology, and especially relates to digital TV, single-carrier OFDM communication system, multi-carrier OFDM communication system, wireless local area network (WLAN), etc. using OFDM modulation. Channel Estimation Techniques in Communication Systems. At the same time, the present invention can not only be used for channel estimation in Code Division Multiple Access (CDMA) and Time Division Multiple Access (TDMA) systems, but the idea of ​​the present invention can also be used in all RLS methods and their derived methods to estimate communication, radar, aerospace, etc. , remote control telemetry, sonar, image processing, computer vision, biomedical engineer...

AI Technical Summary

Problems solved by technology

However, the RLS algorithm also has some inherent defects: in order to reduce the noise in the prediction, when the prediction parameters tend to the true value, the Kalman gain vector in the RLS algorithm is close to 0, which may not track the channel change

When these algorithms are used for channel equalization, they can quickly track the channel when the signal-to-noise ratio is high, and when the signal-to-noise ratio is poor, the channel mutation will be submerged in the noise and the channel cannot be tracked

Literature ParkD.J.etal, Fast trucking RLS algorithm using novel variable forgetting factor with unity zone, Electronics letters [J], 1991, 9, 27 (23): 2150-2151. The RLS algorithm given by the variable forgetting factor is based on Regular RLS, in the process of tracking channel changes, the algorithm derived from adjusting the forgetting factor has the advantages of fast convergence speed and strong tracking ability, but it requires high calculation accuracy of the equipment

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