Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (11): 26-30.doi: 10.16180/j.cnki.issn1007-7820.2021.11.004

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Improvement of RLS Algorithm Based on Regularization Model

SUN Shuai,LIU Zilong,WAN Wei   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200082,China
  • Received:2020-07-04 Online:2021-11-15 Published:2021-11-16
  • Supported by:
    National Natural Science Foundation of China(61603255)


For the clutter signal interference existing in the research of antenna signals, the RLS algorithm is commonly used for filtering processing. However, the forgetting factor of conventional RLS algorithm is usually fixed, which cannot meet the requirements of fast tracking speed and small stability error at the same time. To solve the above problems, this study proposes an improved RLS algorithm based on regularization model. By adding an improved function, the forgetting factor can increase variability, and the inverse matrix equation of the autocorrelation matrix can be improved. The added correlation restriction condition can reduce the noise of the clutter signal to the original signal, and the existence and uniqueness of the noise reduction effect are ensured, thus greatly reducing the interference of the clutter. The waveform diagram of this experiment show that compared with the conventional RLS algorithm, the improved algorithm has stronger tracking capability and stability.

Key words: regularization model, RLS algorithm, improvement factor, autocorrelation matrix, objective function, parameter estimation, convergence speed, tracking ability

CLC Number: 

  • TP391