Design And Implementation Of Frequency Domain Correlation Identification Based On The LabVIEW

LabVIEW is a graphical programming language, VI is its subroutine. It has manyadvantages, for example it has friendly man-machine interface, lively process of programming,great flexibility, great expansibility and so no. Because of these advantages, LabVIEW obtainedmore and more customers approval with to approve sucks. But also LabVIEW has been widelyused in test and measurement, control simulation and other fields.Correlation identification is an important part of system identification, especially in the timedomain identification method has been complete theoretical knowledge and improved methods.It has also been widely used in many fields in the project. The development and maturation ofthe m-sequence further promote the application of the correlation identification. The m-sequenceproduction process is simple; it easy to be implemented in the engineering; it has Idealautocorrelation properties, which is similar with the autocorrelation of white Gaussian noise. Sothe m-sequence is ideal input signal of the correlation identification. The m-sequence as the inputsignal of the correlation identification can reduce the operational difficulty in the recognitionprocess and improve the accuracy of the recognition result.Spectrum estimation is an important means of digital signal processing. It has a long historyand mature theoretical, but also has a wide range of applications in many fields. The emergenceand development of the fast Fourier transform of the spectrum estimation calculation moresimple and fast. Although the fast Fourier has energy leakage shortcomings, but also a largenumber of improvements have been appear. These methods or reduce energy leakage, orcorrecting the calculation results, have led to the spectrum estimation result is more accurate.Correlation identification in the time domain has been developed by leaps and bounds, andthe spectrum estimation has mature theoretical support and a wide range of applications. Basedon this situation, this paper attempts to calculate the correlation identification results from thefrequency domain. In this algorithm, LabVIEW is a simulation platform; m-sequence is the inputsignal; A second-order system that has good waveform both in the frequency domain andtime-domain is the recognition system. We calculate the identification results through theauto-power-spectrum and cross-power-spectrum, and then verify the correctness of the frequencydomain Correlation identification theory. Finally, we study the impact of different parameters onthe recognition results.