1 Introduction In the current testing field, correlation detection methods are increasingly widely used for filtering. Correlation filtering can conveniently separate a specific frequency signal from a complex test signal (including useful signals, DC bias, random noise, and harmonic frequency components). With the rapid development of digital technology, correlation filtering is also often implemented in a computer after sampling the signal using an A/D board, becoming a form of digital filtering. This paper designs a method for implementing correlation filtering, which is a typical application of correlation analysis in testing technology. Figure 1 shows a typical block diagram of a correlation filter. LabVIEW is an integrated program development environment based on a "graphical" approach, launched by National Instruments. It is currently the only compiled graphical programming language internationally. In PC-based measurement and industrial control software, LabVIEW's market penetration rate is second only to C++/C. The LabVIEW development environment has a series of advantages, from flowchart-style programming, the existence of syntax checking and data pointers used in the debugging process without pre-compilation, to its rich functions, numerical analysis, signal processing, and device driver capabilities. Using LabVIEW for filter design is highly efficient, simple to operate, and allows for real-time adjustment of error accuracy. Implementing traditional instruments using LabVIEW in software shortens the development cycle, facilitates maintenance and upgrades, and enables the design of virtual instruments unmatched by traditional instruments. "Software is the instrument"—this is the essence of virtual instrument technology. Simulated Autocorrelation Filter 2. Design of Virtual Correlation Filter 2.1 Front Panel Design Applications developed in the LabVIEW environment are called VIs (Virtual Instruments). A VI is the core of LabVIEW, consisting of a human-computer interaction interface—the front panel—and a block diagram program equivalent to source code—the back panel. The front panel is the program interface, containing two types of objects: control variables and display variables. In the front panel, control variables simulate the instrument's input devices and provide data to the VI's block diagram program, such as switches and knobs; while display variables simulate the instrument's input devices and display data obtained or generated by the block diagram program, such as windows for displaying waveforms. The back panel, also known as the code window or flowchart, is the source code for the VI (Visual Identity) graphical representation. VIs are programmed within the flowchart to control and manipulate input and output functions defined on the front panel. The flowchart includes objects not present on the front panel but essential for programming, such as functions, structures, and connections. The front panel, as shown in Figure 2, consists of the following parts: parameter settings for the reference signal, parameter settings for the signal to be processed, real-time display of the filtering results, and waveforms of the original signal and the filtered result. The amplitude and frequency of the reference signal can be set, as well as the frequency, amplitude, and phase of three sinusoidal signals. After successful program execution, the frequency amplitude and initial phase of the filtered result can be obtained from the real-time filtering display area, and the corresponding waveform can also be obtained from the waveform display area, making the results more intuitive. 2.2 Back Panel Design The back panel, as shown in Figure 3, contains controls corresponding to those in the front panel. Programming is achieved through connections, adding programs, and incorporating various signals to implement the autocorrelation filtering function. Simultaneously, by setting various parameters on the front panel, the program can be successfully run to achieve the desired goal. To achieve this function, various arithmetic units, such as excitation signal sources, filter adders, and multipliers, were added. After running the program, the test results showed that it could extract the desired single frequency signal from a signal containing multiple frequency components, which is equivalent to implementing filtering. Since this filtering approach is based on the definition of the correlation function, it is called a correlation filter. Here, a simulated signal generator was used to simulate the signal under test, which in practice is usually acquired by a data acquisition card. The input signal under test is a superposition of three sine signals, from which a 20 Hz signal needs to be detected. This test VI implements the dynamic display of the correlation filtering process and uses a loop structure. 3 Improved Filter Design Based on the above implementation of correlation filtering, further improvements can be made to simplify the program. The improved front panel is shown in Figure 4, which displays multiple signals in one figure for a more intuitive view. Coarse and fine adjustment knobs have also been added for greater precision. The improved front panel structure is more compact and the design is more reasonable. The improved rear panel is shown in Figure 5. It can be seen that the filter uses a signal average measurement VI instead of the original filter VI. This is because in correlation filtering, the filter's role is essentially to determine the DC component of the test signal; therefore, correlation filtering can be achieved in the same way. Using the correlation function, signals obscured by noise can be identified and extracted, i.e., correlation filtering is performed. With the rapid development of digital technology, correlation filtering is often implemented in a computer after sampling the signal using an A/D board, becoming a form of digital filtering. Implementing correlation filtering in LabVIEW is also a typical application of correlation analysis in testing technology. 4. Conclusion Virtual instruments have become increasingly popular and accepted because virtual instrument systems are faster, simpler, and more convenient, and can also save hardware resources. Through virtual filter design, a deeper understanding and comprehension of virtual instruments can be achieved, providing a foundation for further research.