This paper presents a continuous-time Laguerre-based subspace identification method utilising nuclear norm minimisation. The input–output matrix equation of the systems is deduced by a bank of Laguerre filters in all-pass domain, which can deal with the un-equidistant data. Nuclear norm minimisation is adopted, instead of the truncation of dominant singular values, to obtain low-rank matrix approximations which can easily obtain the system order. Furthermore, the optimisation problem is solved by the alternating direction method of multipliers. Simulation results are provided to show the effectiveness of the proposed method.

中文翻译：

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利用核范数最小化的基于连续时间拉盖尔的子空间识别

本文提出了一种利用核范数最小化的基于连续时间拉盖尔的子空间识别方法。系统的输入输出矩阵方程是由一组全通域的拉盖尔滤波器推导出来的，可以处理非等距数据。采用核范数最小化，而不是截断主导奇异值，得到低秩矩阵近似，可以很容易地获得系统阶数。此外，优化问题是通过乘法器的交替方向方法来解决的。仿真结果表明了所提出方法的有效性。