数字滤波器(3)——C语言的模拟及验证

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之前我们在说明数字滤波器的时候,多为Python来进行示例验证的。实际应用中,多为C/C++,无论是在嵌入式系统中,还是PC机上,尤其对于时间或者实时性要求比较严格的情况下,C/C++应该是我们的首选。

本文通过一个带通滤波器的Python验证,再转换到C++代码模拟验证的实现过程说明数字滤波器是如何工作的。
我们先通过Python测试验证,并生成滤波器的参数数据。然后将获取的参数用到C程序中重现滤波器。
先看图,后查代码。在测试中,我们生成了一个混有50Hz,110Hz和210Hz的模拟信号,然后通过滤波器保留50Hz的信号。

图-1    滤波器的幅频响应(50Hz窄带带通)

图-2    模拟信号的滤波前后

图-3    模拟信号滤波前后的频谱图

相关python代码:


import numpy as np
from scipy.signal import firwin, freqz, lfilter
import matplotlib.pyplot as plt

fs = 1000.0 # Sample frequency (Hz)
f0 = 50.0 # Frequency to be removed from signal (Hz)
Q = 30.0 # Quality factor
w0 = f0/(fs/2) # Normalized Frequency

# Design band-pass filter
b = firwin(81, [w0 - 0.02, w0 + 0.02], pass_zero=False, window='hamming')
# Output coefficients, we got the coefficients from this step
b_string = ', '.join(str(coef) for i, coef in enumerate(b))
print('{', b_string, '}')

# Generate frequency response
w, h = freqz(b, [1], worN=1024)

# Convert to Hz
freq = w * fs / (2 * np.pi)

# Plot filter response
plt.plot(freq, abs(h))
plt.title('Filter Frequency Response')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Gain')
plt.grid(True)
plt.show()

# Create a test signal
t = np.arange(0, 1.0, 1/fs) # Time vector

# Test signal
signal = np.sin(2*np.pi*210*t) + np.sin(2*np.pi*50*t) + np.sin(2*np.pi*110*t)

# Apply filter to the test signal
filtered_signal = lfilter(b, [1], signal)

# Original signal & filtered signal
plt.figure(figsize=(12, 8))
plt.subplot(211)
plt.plot(t[:500], signal[:500], color='blue')
plt.title('Original Signal')
plt.xlabel('Time [s]')
plt.grid()

plt.subplot(212)
plt.plot(t[:500], filtered_signal[:500], color='red')
plt.title('Filtered Signal')
plt.xlabel('Time [s]')
plt.tight_layout()
plt.grid()
plt.show()

# Compute and plot the frequency spectrum of signals
N = len(signal)
T = 1/fs
xf = np.linspace(0.0, 1.0/(2.0*T), N//2) # Frequency vector

# Compute FFT of original and filtered signals
fft_signal = np.fft.fft(signal)
fft_filtered = np.fft.fft(filtered_signal)

# Plot FFT of original signal
plt.figure(figsize=(12, 8))
plt.subplot(211)
plt.plot(xf, 2.0/N * np.abs(fft_signal[0:N//2]), color='blue')
plt.title('Original Signal FFT')
plt.xlabel('Frequency [Hz]')
plt.grid()

# Plot FFT of filtered signal
plt.subplot(212)
plt.plot(xf, 2.0/N * np.abs(fft_filtered[0:N//2]), color='red')
plt.title('Filtered Signal FFT')
plt.xlabel('Frequency [Hz]')
plt.grid()
plt.tight_layout()

plt.show()

 


 

 

  • 滤波器通过C++语言的功能复现和验证。

图-4    模拟信号经C++滤波器的前后波形(取部分,请对照图-2)

这里的测试过程中,模拟信号由代码直接生成,然后经滤波器处理后,将该模拟信号和经滤波之后的信号数据全部存到csv文件中。在csv文件中,我们可以再现数据滤波前后的变化。

以下为滤波器的C++代码,大家可以再优化。直接上代码。



#include <stdio.h>
#include <iostream>
#include <vector>
#include <fstream>
#include <math.h>

#define SAMPLE_RATE 1000.0

using namespace std; // 声明使用std命名空间

const double pi = 3.14159265358979323846;

// 模拟信号函数
vector<double> generateSignal(int sampleRate, int seconds){
  vector<double>signal(sampleRate * seconds);   //定义模拟信号的数组长度
  for (unsigned int i = 0; i < (unsigned int)(sampleRate * seconds); ++i){
    // 包含50Hz,110Hz和210Hz信号
    signal[i] = sin((2 * pi * i * 50) / sampleRate) + sin((2 * pi * i * 210) / sampleRate) + sin((2 * pi * i * 110) / sampleRate);
  }
  return signal;
}

// 滤波器函数
vector<double> filter(const vector<double>& b, const vector<double>& a, const vector<double> &signal){
  vector<double> output(signal.size());
  for (size_t i = 0; i < signal.size(); ++i)
  {
    for (size_t j = 0; j < b.size(); ++j){
      if (i >= j){
        output[i] += b[j] * signal[i - j];
      }
    }
    for (size_t j = 1; j < a.size(); ++j){
      if (i >= j){
        output[i] -= a[j] * output[i - j];
      }
    }
    output[i] /= a[0];
  }
  return output;
}

// 写入文件函数
void writeToFile(const vector<double>& signal, const vector<double>& filtered_signal, const string &filename)
{
  ofstream file(filename);

  for (std::size_t i = 0; i < signal.size(); i++)
  {
    file << i/SAMPLE_RATE << ", " << signal[i] <<", "<< filtered_signal[i]<< "\n";
  }
}
// 主函数
int main()
{
  // 系数
vector<double>b{0.0010175493084400998,0.0010954624020866333,         0.001080635650435545, 0.0009293052645812359,0.0005868808563577278, -8.138309855847798e-19, -0.0008644147524968251, -0.0019966389877814107, -0.003323586744207458, -0.004696461345361978, -0.005892320462621699, -0.006633249964255378, -0.006623614506478284, -0.005601944833604465, -0.0034001773970723163, -7.334366341273803e-18,  0.004425290874832446, 0.00949426225087417, 0.014634010415364655, 0.019132982942933127,  0.022226796444847933, 0.023207550009729024, 0.021541722692400025, 0.01697833945185371,   0.009628503914736117, -6.755395515820625e-18, -0.01102370844120733, -0.02226281209657117,   -0.032372473621654914, -0.04001099412924139, -0.04402269970024527, -0.043609484958132556, -0.03846490807520255, -0.028848803480728435, -0.015588116829396594, -9.10410551538968e-18, 0.016255406162706088, 0.031374390998733945, 0.04363491329762711, 0.051616779739690075, 0.05438594145724075, 0.051616779739690075, 0.04363491329762711, 0.031374390998733945,  0.016255406162706088, -9.10410551538968e-18, -0.015588116829396594, -0.028848803480728435,  -0.03846490807520255, -0.043609484958132556, -0.04402269970024527, -0.0400109941292414, -0.032372473621654914, -0.022262812096571168, -0.01102370844120733, -6.755395515820627e-18,   0.009628503914736117, 0.016978339451853702, 0.021541722692400025, 0.023207550009729034,  0.022226796444847933, 0.01913298294293312, 0.014634010415364655, 0.009494262250874175, 0.004425290874832446, -7.3343663412738e-18, -0.0034001773970723163, -0.005601944833604469,  -0.006623614506478284, -0.006633249964255374, -0.005892320462621699, -0.00469646134536198, -0.003323586744207458, -0.001996638987781409, -0.0008644147524968251, -8.138309855847805e-19, 0.0005868808563577278, 0.0009293052645812359, 0.001080635650435545, 0.0010954624020866333,  0.0010175493084400998};

vector<double> a{1};

  // 生成模拟信号
vector<double> signal = generateSignal(1000, 1); // 1秒的模拟信号

  // 滤波处理
vector<double> output = filter(b, a, signal);

  // 写入至csv文件
writeToFile(signal, output, "output.csv");

  return 0;
}

 


 

从模拟到应用,我们的传感器会根据大家的需求不断演进。
 

 

 

2024年6月12日 09:53