Introduction In a wide range of scientific and technical fields such as vibration, acoustics, earthquakes, communications, radar, control systems, and biomedical engineering, the requirements of filtering and spectrum analysis are put forward for the signals actually observed. The content of processing them in digital form constitutes the basic research content of digital signal processing. Filtering technology has a wide range of applications in virtual instruments. The signals collected from the test site contain useful signals, unwanted signals, and noise for data processing. The purpose of filtering is to extract useful signals from the signals. In the virtual instrument system, the signal is collected into the computer, usually also need to use software to complete complex analysis and signal processing work, LabVIEW provides a large number of analysis tools, mature algorithms, which facilitates the development of software. Based on this, this paper simulates and generates a noisy signal close to the actual signal in the PC LabVIEW software, and generates a noisy signal generating device. The IIR filter with faster filtering speed is selected, and the virtual filter is designed to remove noise.
1. Design idea In the process of signal transmission, high-frequency noise is often mixed, and the energy of the noise will even exceed the signal energy. Therefore, after receiving the signal, the receiver usually performs low-pass filtering first, and then can further process the signal. The flow chart of the design is shown in Figure 1:
2. Design Principle of Virtual Filter to Remove Noise VI 2.1 Generating Filter's Self-selection Signal The self-selection signal is realized by the signal from the signal generator plus the noise signal. In the signal generator part, Basic Function Generator.vi is used to generate standard signals such as sine wave, square wave, triangle wave, sawtooth wave, etc. In the noise part, I use Uniform White Noise Waveform.vi to generate uniformly distributed pseudo-random white noise, and then phase together Add to generate optional signal.
Figure 1 Virtual filter VI flow chart
In this program, set Fs (sampling frequency) and #s (number of sampling points) to be 1000. The frequency of the signal generated according to the Nyquist sampling theorem is less than Fs / 2, and the frequency spectrum will not be distorted, so Basic FuncTIon Generator. The frequency of the signal generated by vi should not exceed 500 Hz. But after testing, the sampling frequency in LabVIEW is easy to be distorted if it is closer to Fs / 2 spectrum. Therefore, the frequency of the signal generated by Basic FuncTIon Generator.vi must be selected according to the actual use.
The amplitude of the noise is between [-a, a], a is the absolute value of the amplitude, and the default value is set to 0.10 this time. Since the generated noise output signal is to be added to the generated signal of Basic FuncTIon Generator.vi, the Sampling info (sampling information) is the same as the Sampling info of Basic FuncTIon Generator.vi. The output signal out is also a cluster, which contains three data of to, Y, and dt. The function of each data is the same as the signal out in Basic Function Generator.vi. The signals output from the signal out of Basic Function Generator.vi and Uniform White Noise Waveform.vi each go through a get waveform component.vi to get the waveform component Y. Finally, the signals generated by the two go through the get waveform component.vi and then an Add to get the simulated real signal. As shown in Figure 2, the error in (out) of the two can be connected when the connection is prepared for the final total program. Error reporting system.
Figure 2 Partial program diagram of the optional signal
2.2. Filter section
The filtering part is the core part of this program. IIR filter with fast filtering speed and no requirement for phase is selected. This program uses Butterworth filter and Chebyshev filter. Butterworth filters have the smoothest frequency response, and the frequency response drops monotonically beyond the cutoff frequency. It is the ideal unit response in the pass band, and the response is zero in the stop band. The steepness of the transition band is proportional to the order of the filter, so the filtering effect can be significantly improved by increasing the order. And can choose low-pass filtering, high-pass filtering, band-pass filtering or band-stop filtering, users can choose the appropriate filtering mode according to their own filtering requirements. The realization of this function is completed with a case selection structure.
On the front panel of the program, a drop-down list is used to realize the function of selecting different filter modes. A total of nine different modes are set. When we want to observe the shape of the self-selected signal without filtering, we can choose to turn off the filter. When we observe the filtering effect under different filters, we can switch to different modes to observe. The program chart of the filtering part is shown in Figure 3.
Figure 3 The function of the filtering part ImgZoom (Id) // Re-set the image size to prevent breaking the table {var w = $ (Id) .width; var m = 650; if (w <m) {return;} else {var h = $ (Id) .height; $ (Id) .height = parseInt (h * m / w); $ (Id) .width = m;}} window.onload = function () {var Imgs = $ ("content" ) .getElementsByTagName ("img"); var i = 0; for (; i
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