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In thisRandomly Occurring Quantization (ROQ) chapter, the optimized recursive filteringRecursive filter issue is discussed for networked time-varying nonlinear systemsTime-varying nonlinear system with ROQ. A sequence of binary random variables is used to describe the ROQ, in which the logarithmic quantizerLogarithmic quantizer is employed to describe the phenomenon of quantized measurements. The purpose of this chapter is to construct a desirable filter of recursive form such that, for all ROUs, ROQ and stochastic nonlinearityNonlinearity, an upper boundUpper bound regarding the FECFiltering Error Covariance (FEC) is solved and the expected filter gainFilter gain with expression form is derived and given. Furthermore, a sufficient conditionSufficient condition is provided to guarantee the boundednessBoundedness of the filtering errorFiltering error dynamicsFiltering error dynamics. Lastly, some comparative simulation examples are provided to validate the usefulness of established recursive filteringRecursive filter algorithm.

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Recursive Filtering and Boundedness Analysis with Randomly Occurring Quantization

  • 章节

  • 发表日期：

    2025年

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  • Jun Hu,
  • Zidong Wang,
  • Chaoqing Jia

摘要

In thisRandomly Occurring Quantization (ROQ) chapter, the optimized recursive filteringRecursive filter issue is discussed for networked time-varying nonlinear systemsTime-varying nonlinear system with ROQ. A sequence of binary random variables is used to describe the ROQ, in which the logarithmic quantizerLogarithmic quantizer is employed to describe the phenomenon of quantized measurements. The purpose of this chapter is to construct a desirable filter of recursive form such that, for all ROUs, ROQ and stochastic nonlinearityNonlinearity, an upper boundUpper bound regarding the FECFiltering Error Covariance (FEC) is solved and the expected filter gainFilter gain with expression form is derived and given. Furthermore, a sufficient conditionSufficient condition is provided to guarantee the boundednessBoundedness of the filtering errorFiltering error dynamicsFiltering error dynamics. Lastly, some comparative simulation examples are provided to validate the usefulness of established recursive filteringRecursive filter algorithm.