Common Filtering Methods for Microcontroller ADC

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However, the analog signals collected by our microcontroller’s ADC generally undergo “filtering” processing before they can be used. Below, we will share some common ADC filtering algorithms.

1. Clipping Filter

1. Method

  • Determine the maximum allowable deviation A based on experience.

  • When sampling a new value, check: if the difference between the current value and the previous value is <= A, then the current value is valid; if the difference is > A, the current value is invalid, and the previous value is used instead.

2. Advantages and Disadvantages

  • Overcomes pulse interference but cannot suppress periodic interference; smoothness is poor.

3. Code

/* A value is adjusted based on actual conditions, Value is the valid value, new_Value is the current sampled value, the program returns the valid actual value */
#define A 10
char Value;
char filter()
{
  char new_Value;
  new_Value = get_ad();                                        // Get sampled value
  if( abs(new_Value - Value) > A)   return Value;             // abs() function to get absolute value
  return new_Value;
}

2. Median Filter

1. Method

  • Continuously sample N times and arrange in order.

  • Take the middle value as the valid value for this sampling.

2. Advantages and Disadvantages

  • Overcomes fluctuation interference, has good filtering effect for slowly changing measured parameters like temperature, but is not suitable for rapidly changing parameters like speed.

3. Code

#define N 11
char filter()
{
 char value_buf[N];
 char count,i,j,temp;
 for(count = 0;count < N;count++)                                // Get sampled values
 {
  value_buf[count] = get_ad();
  delay();
 }
 for(j = 0;j<(N-1);j++)
  for(i = 0;i<(N-j);i++)
  if(value_buf[i]>value_buf[i+1])
  {
   temp = value_buf[i];
   value_buf[i] = value_buf[i+1];
   value_buf[i+1] = temp;
  }
 return value_buf[(N-1)/2];
}

3. Arithmetic Mean Filter

1. Method

  • Continuously sample N times and take the average.

  • When N is large, smoothness is high but sensitivity is low.

  • When N is small, smoothness is low but sensitivity is high.

  • Generally, N=12.

2. Advantages and Disadvantages

  • Suitable for systems with random interference, consumes a lot of RAM, and is slow.

3. Code

#define N 12
char filter()
{
 int sum = 0;
 for(count = 0;count<n;count++) (char)(sum="" +="get_ad();" <="" code="" n);="" return="" sum="" }=""></n;count++)>

4. Recursive Average Filter

1. Method

  • Take N sampled values to form a queue, first in first out.

  • Take the average.

  • Generally, N=4~12.

2. Advantages and Disadvantages

  • Good suppression of periodic interference, high smoothness.

  • Suitable for high-frequency vibration systems.

  • Low sensitivity, high RAM usage, and severe pulse interference.

3. Code

/* A value is adjusted based on actual conditions, Value is the valid value, new_Value is the current sampled value, the program returns the valid actual value */
#define A 10
char Value;
char filter()
{
  char new_Value;
  new_Value = get_ad();                                        // Get sampled value
  if( abs(new_Value - Value) > A)   return Value;             // abs() function to get absolute value
  return new_Value;
}

5. Median Average Filter

1. Method

  • Sample N values, remove the maximum and minimum.

  • Calculate the average of N-2.

  • N= 3~14.

2. Advantages and Disadvantages

  1. Combines the advantages of median and average values.

  2. Eliminates pulse interference.

  3. Slow computation speed and high RAM usage.

3. Code

char filter()
{
 char count,i,j;
 char Value_buf[N];
 int sum=0;
 for(count=0;count<n;count++) for(i="0;i<(N-j);i++)" for(j="0;j<(N-1);j++)" if(value_buf[i]="" value_buf[count]="get_ad();">Value_buf[i+1])
   {
     temp = Value_buf[i];
     Value_buf[i]= Value_buf[i+1];
      Value_buf[i+1]=temp;
   }
   for(count =1;count<n-1;count++) (char)(sum="" (n-2));="" +="Value_buf[count];" <="" code="" return="" sum="" }=""></n-1;count++)></n;count++)>

6. Clipping Average Filter

1. Method

  • Limit the sampled data before sending it to the queue.

  • Take the average.

2. Advantages and Disadvantages

  • Combines the advantages of clipping, averaging, and queuing.

  • Eliminates pulse interference but consumes a lot of RAM.

3. Code

#define A 10
#define N 12
char value,i=0;
char value_buf[N];
char filter()
{
 char new_value,sum=0;
 new_value=get_ad();
 if(Abs(new_value-value)<a) (char)(sum="" ;count<n;count++)="" <="" code="" for(count="0" if(i="N)i=0;" n);="" return="" sum+="value_buf[count];" value_buf[i++]="new_value;" }=""></a)>

7. First-Order Lag Filter

1. Method

  • Take a=0~1.

  • The filtering result = (1-a) * current sample + a * previous result.

2. Advantages and Disadvantages

  • Good for periodic interference, suitable for situations with high fluctuation frequency.

  • Low sensitivity and phase lag.

3. Code

/* To speed up program processing, take a=0~100 */
#define a 30
char value;
char filter()
{
 char new_value;
 new_value=get_ad();
 return ((100-a)*value + a*new_value);
}

8. Weighted Recursive Average Filter

1. Method

  • An improvement on the recursive average filter, applying different weights to data at different times, usually with newer data having greater weight, resulting in high sensitivity but low smoothness.

2. Advantages and Disadvantages

  • Suitable for systems with large lag time constants and short sampling periods; cannot quickly respond to signals with small lag time constants, long sampling periods, and slow changes.

3. Code

/* coe array is the weighting coefficient table */
#define N 12
char code coe[N]={1,2,3,4,5,6,7,8,9,10,11,12};
char code sum_coe={1+2+3+4+5+6+7+8+9+10+11+12};
char filter()
{
 char count;
 char value_buf[N];
 int sum=0;
 for(count=0;count<n;count++) (char)(sum="" <="" code="" for(count="0;count<N;count++)" return="" sum+="value_buf[count]*coe[count];" sum_coe);="" value_buf[count]="get_ad();" {="" }=""></n;count++)>

9. Debouncing Filter

1. Method

  • Set a filtering counter.

  • Compare the sampled value with the current valid value.

  • If the sampled value equals the current valid value, reset the counter to 0.

  • If the sampled value does not equal the current valid value, increment the counter by 1.

  • If the counter overflows, replace the current valid value with the sampled value and reset the counter to 0.

2. Advantages and Disadvantages

  • Good filtering effect for slowly changing signals, poor for rapidly changing ones.

  • Avoids fluctuations near critical values; if an interference value is sampled when the counter overflows, it cannot be filtered.

3. Code

#define N 12
char filter()
{
 char count=0,new_value;
 new_value=get_ad();
 while(value!=new_value)
 {
  count++;
  if(count>=N) return new_value;
  new_value=get_ad();
 }
 return value;
}

10. Clipping Debouncing Filter

1. Method

  • Limit first, then debounce.

2. Advantages and Disadvantages

  • Combines the advantages of clipping and debouncing.

  • Avoids introducing interference values; not suitable for rapidly changing signals.

3. Code

#define A 10
#define N 12
char value;
char filter()
{
 char new_value,count=0;
 new_value=get_ad();
 while(value!=new_value)
 {
  if(Abs(value-new_value)<a) count++;="" if(count="" {="">=N) return new_value;
  new_value=get_ad();
  }
 return value;
 }
}
</a)>

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