This article considers the parameter estimation problems of linear multivariable systems with unknown disturbances. For the parameter matrices in the multivariable systems, the model decomposition technique is used to reduce the computational complexity by decomposing the multivariable system into several subsystems with only the parameter vectors. By means of the negative gradient search, a decomposition-based maximum likelihood recursive extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, by introducing the multi-innovation identification theory, a decomposition-based maximum likelihood multi-innovation extended stochastic gradient algorithm is proposed. The simulation results illustrate the effectiveness of the proposed algorithms.

中文翻译：

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基于最大似然原理的多变量系统的多创新梯度参数估计

本文考虑了具有未知扰动的线性多变量系统的参数估计问题。对于多变量系统中的参数矩阵，采用模型分解技术，通过将多变量系统分解为几个仅包含参数向量的子系统，从而降低计算复杂度。通过负梯度搜索，导出了一种基于分解的最大似然递归扩展随机梯度算法。为提高参数估计精度，通过引入多重创新识别理论，提出了一种基于分解的最大似然多重创新扩展随机梯度算法。仿真结果说明了所提出算法的有效性。