目录
- 一、Matlab中的FIR实验
- 1.搭建原始信号
- 2.设计FIR滤波器
- 3.FIR滤波
- 4.滤波前后对比
- 二、嵌入式平台FIR滤波移植
- 1.arm-dsp库移植
- 2.获得FIR滤波系数
- 3.基于arm-dsp的FIR程序
- 4.滤波结果-与Matlab比对
- 三、总结
电力电子应用中,往往需要对电流、电压信号进行采样,滤除干扰频段的信号,或者保留、提取感兴趣的频段的信号。通过模拟滤波往往难以达到较好的效果,因此需要采用数字滤波器。
常用的数字滤波器有IIR、FIR滤波器两种。FIR 滤波器最重要的优点就是由于不存在系统极点,FIR 滤波器是绝对稳定的系统。FIR 滤波器还确保了线性相位,这在信号处理中也非常重要。此外,由于不需要反馈,FIR 滤波器的实现也比 IIR 滤波器简单。FIR 滤波器的缺点在于它的性能不如同样阶数的 IIR 滤波器,不过由于数字计算硬件的飞速发展,这一点已经不成为问题。再加上引入计算机辅助设计,FIR 滤波器的设计也得到极大的简化。基于上述原因,FIR 滤波器比 IIR 滤波器的应用更广泛。
这篇博文就通过Matlab仿真来直观的介绍FIR滤波器的用途,并将FIR滤波器移植到以STM32F4为基础的嵌入式平台中。
一、Matlab中的FIR实验
通过一个简单的实验来使用FIR滤波器:将200Hz正弦信号叠加到基波50Hz的正弦信号上,然后利用FIR滤波器提取200Hz信号。采样率设置为6400Hz。
1.搭建原始信号
在simulink搭建简易混合电路,将幅值为1的50Hz正弦波与幅值为1的200Hz正弦波混合。
首先设置simulink的仿真步长为固定步长,1/6400,那么就相当于采样率为6400Hz,如图所示。
搭建电路如下图所示。其中1为多路复合,为了在scope中分别查看50Hz和200Hz的原始信号,2为信号叠加混合,即50Hz混合200Hz的信号。
运行0.2秒,即10个50Hz周波,查看scope窗口如下图所示。可以看到上方为50Hz、200Hz的原始信号,下方为混合后的信号。我们现在要做的就是利用FIR滤波器将200Hz信号从下方的信号中提取出来。将scope数据导出到workspace待用。
2.设计FIR滤波器
在matlab命令行输入fdatool,打开滤波器设计工具。
1.选择带通滤波、FIR窗口滤波;
2.选择常用的汉宁窗;
3.指定FIR阶数,一般来讲,阶数越高,滤波效果越好,但运算量越大,需要在滤波效果和运行速度上做一个取舍,这里我们暂时设置为100阶;
4.设置采样频率为6400Hz,要与simulink中的相符;
5.设置带通范围,这里我们设置为带通范围180~220Hz;
6.点击Design Filter;
7.点击Group Delay Response可以查看滤波器的相位延迟,这里是50,这个数要记住,后边要用到。
生成滤波器后,我们将滤波器导出到workspace中。具体操作为:file->export->Export As选择Objects。默认的滤波器名字为Hd。
3.FIR滤波
至此,worksapce中已经有了scope中导出的原始波形、混杂波形,以及设计好的滤波器Hd。
在命令行中对混杂信号进行滤波:
fileter(Hd,rawSignal)
其中rawSignal为混杂信号,即scope中下方的波形。运行后,ans中就存储了滤波后的波形。
4.滤波前后对比
将滤波后的波形与原始的200Hz信号进行比对:
plot(1:200,ans(51:250),1:200,raw200Hz(1:200))
其中,滤波后的信号要从51开始,因为滤波器在设计时的FIR相位延迟为50,即第51个点才为滤波后的数据起始点。raw200Hz为从scope中导出的200Hz信号数据。绘图后如下图所示。可以看出FIR滤波震荡稳定后,能较好的带通200Hz信号,幅值、相位均与原信号相同,50Hz信号被滤掉。
二、嵌入式平台FIR滤波移植
1.arm-dsp库移植
arm-dsp库的移植可以参考我之前的博客【嵌入式】利用arm-DSP库进行FFT计算,获得信号的频谱、幅值及相位(上)中的二-1节内容。
2.获得FIR滤波系数
还是在fdatool中,当设计玩滤波器后,点击Targets->Generate C Header.
打开生成的fdacoefs.h,里边的BL即为FIR滤波器阶数。B数组即为FIR滤波器系数,将前边的数据类型real64_T改为float即可。
/** Filter Coefficients (C Source) generated by the Filter Design and Analysis Tool* Generated by MATLAB(R) 8.3 and the Signal Processing Toolbox 6.21.* Generated on: 28-Apr-2021 15:33:18*//** Discrete-Time FIR Filter (real)* -------------------------------* Filter Structure : Direct-Form FIR* Filter Length : 101* Stable : Yes* Linear Phase : Yes (Type 1)*//* General type conversion for MATLAB generated C-code */
#include "tmwtypes.h"
/* * Expected path to tmwtypes.h * F:\MatLab\extern\include\tmwtypes.h */
const int BL = 101;
const real64_T B[101] = {-0.002364379994766,-0.002555816813742, -0.00271143828077,-0.002821346700614,-0.002865814202786,-0.002816683118228,-0.002640000779638,-0.002299699197689,-0.001762028066288,-0.001000368319048,4.399291056296e-18, 0.001237622441001,0.002691519514449, 0.004319142530531, 0.006055934652271, 0.007816685262717,0.009498734985135, 0.01098694698435, 0.01216021777687, 0.01289916830821,0.01309454373426, 0.01265576722454, 0.01151904640175, 0.009654425543614,0.007071214384679, 0.003821304224151,-5.964020643576e-18,-0.004255853855223,-0.008772504883931, -0.01334655058446, -0.01775451146912, -0.02176424083345,-0.02514755140538, -0.02769333338392, -0.02922037668388, -0.0295890978537,-0.02871141129903, -0.02655807388539, -0.02316296707884, -0.01862395375005,-0.01310014718858, -0.00680564532364,-1.977442795623e-17, 0.007024104515362,0.01395531112556, 0.02047939405639, 0.02629552027152, 0.03113204072429,0.03476090062898, 0.03700983073789, 0.03777160643401, 0.03700983073789,0.03476090062898, 0.03113204072429, 0.02629552027152, 0.02047939405639,0.01395531112556, 0.007024104515362,-1.977442795623e-17, -0.00680564532364,-0.01310014718858, -0.01862395375005, -0.02316296707884, -0.02655807388539,-0.02871141129903, -0.0295890978537, -0.02922037668388, -0.02769333338392,-0.02514755140538, -0.02176424083345, -0.01775451146912, -0.01334655058446,-0.008772504883931,-0.004255853855223,-5.964020643576e-18, 0.003821304224151,0.007071214384679, 0.009654425543614, 0.01151904640175, 0.01265576722454,0.01309454373426, 0.01289916830821, 0.01216021777687, 0.01098694698435,0.009498734985135, 0.007816685262717, 0.006055934652271, 0.004319142530531,0.002691519514449, 0.001237622441001,4.399291056296e-18,-0.001000368319048,-0.001762028066288,-0.002299699197689,-0.002640000779638,-0.002816683118228,-0.002865814202786,-0.002821346700614, -0.00271143828077,-0.002555816813742,-0.002364379994766
};
3.基于arm-dsp的FIR程序
1.将混杂信号采样值复制到C数组raw中。
const float raw[1280] = {0,0.244157996343546,0.480700572694650,0.702300707474964,0.902197103202676,1.07444979220581,1.21416420976575,1.31767513379545,1.38268343236509,1.40834037383351,
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2.利用arm_fir_init_f32和arm_fir_f32进行FIR计算。
首先讲解arm_fir_init_f32 FIR初始化函数,其函数原型为
void arm_fir_init_f32(arm_fir_instance_f32 * S,uint16_t numTaps,float32_t * pCoeffs,float32_t * pState,uint32_t blockSize)
其中:S为fir实例指针;numTaps为FIR阶数;pCoeffs为FIR系数指针;pState为状态数组指针,其实就是一个计算过程中需要开辟的缓存,用于计算,其长度为numTaps+blockSize-1;blockSize为计算块的长度,大于1,小于等于原始数据的总长度即可。
配置好arm_fir_init_f32后,即可使用arm_fir_f32进行FIR计算,arm_fir_f32的函数原型为
void arm_fir_f32(
const arm_fir_instance_f32 * S,
float32_t * pSrc,
float32_t * pDst,
uint32_t blockSize)
由于设置了blockSize,即每次调用arm_fir_f32计算的数据长度,如blockSize设置为128时,要对总长度为1280的原始数据进行计算,就要循环调用10次arm_fir_f32,其中pSrc和pDst指针也要随着循环进行后移。
具体FIR程序如下所示:
float32_t output[1280] = {0};
void FIR()
{uint32_t timeTick = HAL_GetTick(); //函数进入时间戳uint32_t timePass = 0; //计算用时uint32_t blockSize = 128; //块长度,原始数据长度为1280uint32_t numOfBlock = 1280 / blockSize; //uint32_t i;arm_fir_instance_f32 S;float32_t *pState;pState = (float32_t *)Mem.malloc((blockSize + BL - 1) * sizeof(float32_t));if(pState != NULL){arm_fir_init_f32(&S,BL,(float32_t *)B,pState,blockSize);for(int i=0; i < numOfBlock; i++){arm_fir_f32(&S, raw + (i * blockSize), output + (i * blockSize), blockSize);}}timePass = HAL_GetTick() - timeTick;Mem.free(pState);}
其中,timePass保存了计算所需要的时间;output为FIR滤波后的数据输出数组;设置blockSize为128,则numOfblock块个数就是raw数组的长度1280除以128;pState为临时使用内存池申请的缓存,如果你的程序中没有内存池,可以直接开辟一个长度为1280的float数组,或者直接使用malloc动态申请。
运行后,timePass等于2,说明对1280个点进行100阶的FIR滤波只需要2ms,速度已经非常快了。
4.滤波结果-与Matlab比对
将output数组数据导出,导入到matlab中,与matlab的FIR滤波结果进行比对,发现一模一样完全重合,证明了stm32调用arm-dsp库FIR滤波的准确性、可靠性和高效性。
plot(1:1280,ans(1:1280),1:1280,stm32out)
三、总结
实际工程应用中,往往是单片机对信号进行采样,将波形保存在一个数组中,然后再使用FIR进行滤波处理。一般的工程开发过程为:
1.分析原始信号,确定滤波目标;
2.导出单片机采样数据,在matlab中设计合适的FIR滤波器;
3.导出Matlab滤波器系数到单片机程序;
4.利用arm-dsp库进行FIR滤波计算。