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LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

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Abstract

This paper is concerned with the parameter estimation of deterministic autoregressive moving average (DARMA) systems with quantization data. The estimation algorithms adopted here are the least squares (LS) and the forgetting factor LS, and the signal quantizer is of uniform, that is, with uniform quantization error. The authors analyse the properties of the LS and the forgetting factor LS, and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error, which implies that the smaller the quantization error is, the smaller the estimation error is. A numerical example is given to demonstrate theorems.

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Authors and Affiliations

  1. Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Lida Jing & Ji-Feng Zhang

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Lida Jing & Ji-Feng Zhang

Authors
  1. Lida Jing
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  2. Ji-Feng Zhang
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Corresponding author

Correspondence to Ji-Feng Zhang.

Additional information

The work was supported by National Key R&D Program of China under Grant No. 2018YFA0703800; the National Natural Science Foundation of China under Grant No. 61877057.

This paper was recommended for publication by Editor WU Zhengguang.

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Jing, L., Zhang, JF. LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations. J Syst Sci Complex 35, 748–765 (2022). https://doi.org/10.1007/s11424-021-0314-y

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  • DOI: https://doi.org/10.1007/s11424-021-0314-y

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