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PERFORMANCE OF FIR ADAPTIVE FILTERS USING RECURSIVE ALGORITHMS.

机译：使用递归算法的FIR自适应滤波器的性能。

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摘要

Adaptive digital filters used for speech and data processing are investigated. The algorithms of interest use either a least mean square (LMS) or least squares (LS) criterion and adapt the filter coefficients recursively in time. Only the all-zero transversal and lattice filter structures are considered. These types of algorithms have numerous applications including channel equalization; however, application to speech waveform coding is emphasized. A geometric or Hilbert space formalism is used to derive the LMS lattice, LS lattice, and "Fast" Kalman algorithms in a cohesive manner. Convergence properties of the LMS adaptive lattice filter are subsequently discussed. First-order expressions for single-stage convergence time and output mean squared error are obtained. A simple deterministic model for multi-stage convergence is then described which gives filter coefficient mean-values and output mean squared error as functions of time. Results obtained for the LMS lattice are also extended to the LS lattice. The model of convergence for the LMS lattice (predictor) is then extended to the LMS and LS lattice joint process estimators and to the "Fast" Kalman algorithm. In each case calculated curves obtained from the model are compared with simulation results.;Finally, an empirical comparison is made between the performance of each adaptive predictor in the context of adaptive differential pulse code modulation (ADPCM) of speech. A novel configuration consisting of an LS lattice predictor combined with a least squares lattice pitch detector to remove pitch redundancy is also tested. Our results describe the tradeoff between improved performance vs. increasing complexity.
机译：研究了用于语音和数据处理的自适应数字滤波器。感兴趣的算法使用最小均方（LMS）或最小均方（LS）准则，并在时间上递归调整滤波器系数。仅考虑全零横向和晶格滤波器结构。这些类型的算法具有众多应用，包括信道均衡；然而，强调了在语音波形编码中的应用。使用几何或希尔伯特空间形式主义以内聚的方式导出LMS晶格，LS晶格和“快速”卡尔曼算法。随后讨论LMS自适应晶格滤波器的收敛特性。得到单级收敛时间和输出均方误差的一阶表达式。然后，描述了一种用于多级收敛的简单确定性模型，该模型给出了滤波器系数的平均值和输出均方误差作为时间的函数。 LMS晶格获得的结果也扩展到LS晶格。然后将LMS晶格（预测变量）的收敛模型扩展到LMS和LS晶格联合过程估计量，并扩展到“快速”卡尔曼算法。在每种情况下，将从模型获得的计算曲线与仿真结果进行比较。最后，在语音的自适应差分脉冲编码调制（ADPCM）的情况下，对每个自适应预测器的性能进行了经验比较。还测试了一种新颖的配置，该配置由LS晶格预测器与最小二乘晶格间距检测器组合而成，可以消除间距冗余。我们的结果描述了性能提高与复杂性之间的权衡。

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  • 作者

    HONIG, MICHAEL LATHAM.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 1981
  • 页码 357 p.
  • 总页数 357
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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