A CS Recovery Algorithm for Model and Time Delay Identification of MISO-FIR Systems

Algorithms

Yanjun Liu¹  Taiyang Tao² 

[1] Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; E-Mail:

关键词: compressed sensing;    sparse;    parameter identification;    gradient projection pursuit algorithm;    time delay estimation;   

DOI  :  10.3390/a8030743

来源: mdpi

【 摘 要 】

This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different algorithms. In this paper, we aim to perform the model identification and time delay estimation simultaneously from a limited number of observations. For an MISO-FIR system with many inputs and unknown input time delays, the corresponding identification model contains a large number of parameters, requiring a great number of observations for identification and leading to a heavy computational burden. Inspired by the compressed sensing (CS) recovery theory, a threshold orthogonal matching pursuit algorithm (TH-OMP) is presented to simultaneously identify the parameters, the orders and the time delays of the MISO-FIR systems. The proposed algorithm requires only a small number of sampled data compared to the conventional identification methods, such as the least squares method. The effectiveness of the proposed algorithm is verified by simulation results.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.