Separable Synchronous Multi-Innovation Gradient-Based Iterative Signal Modeling From On-Line Measurements

This article is aimed to study the modeling problems of combinational signals or periodic signals. To overcome the computation complexity of modeling the signals with plenty of characteristic parameters, a parameter separation scheme is developed based on the different characteristic of the signals to be modeled. For the purpose of achieving high-accuracy performance and reducing complexity, two multi-innovation gradient-based iterative (MIGI) subalgorithms are presented by means of gradient search. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous (SS) interactive estimation method is proposed to eliminate the coupling parameters and perform the signal modeling algorithm in accordance with the hierarchical principle. By means of simulation experiments, the proposed SS iterative signal modeling algorithm based on the moving batch data is used for estimating a power signal with three sine waves and a periodic square wave signal. The results demonstrate the effectiveness of the proposed method for modeling the combinational signals with multiple frequencies and other periodic signals. Since the proposed method combines real-time data sampling and iterative estimation, it can be used for on-line identification.

Automated summary

This article presents a high-precision signal modeling algorithm based on a parameter separation scheme for combinational signals and periodic signals, implemented through gradient search. Research shows that the SS iterative signal modeling algorithm can effectively estimate combinational signals with multiple frequencies and periodic signals.