1. Clipping Filter Method
1. Method:
- Determine the maximum allowable deviation between two samples based on experience (denote as A)
- When a new value is detected, determine:
a. If the difference between the current value and the previous value <= A, then the current value is validb. If the difference between the current value and the previous value > A, then the current value is invalid, discard the current value and use the previous value instead2. Advantages:
- Can effectively overcome pulse interference caused by random factors
3. Disadvantages:
- Cannot suppress periodic interference
- Poor smoothness
/* A value is adjusted based on actual conditions, Value is the valid value, new_Value is the current sample value, the program returns the valid actual value */#define A 10char Value;char filter(){ char new_Value; new_Value = get_ad(); // Get sample value if( abs(new_Value - Value) > A) return Value; // abs() function to get absolute value return new_Value;}2. Median Filter Method1. Method:
- Continuously sample N times (N is an odd number), and arrange the N sampled values in order
- Take the middle value as the valid value for this time
2. Advantages:
- Can effectively overcome fluctuations caused by random factors
- Has a good filtering effect on slowly changing parameters such as temperature and liquid level
3. Disadvantages:
- Not suitable for rapidly changing parameters such as flow and speed
#define N 11char filter(){ char value_buf[N]; char count, i, j, temp; for(count = 0; count < N; count ++) // Get sample 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 Method1. Method:
- Continuously take N sample values for arithmetic averaging
- When N is large: the signal smoothness is high, but sensitivity is low
- When N is small: the signal smoothness is low, but sensitivity is high
- Selection of N: generally for flow, N=12; for pressure, N=4
2. Advantages:
- Suitable for filtering signals with random interference
- Such signals have an average value, fluctuating around a certain value range
3. Disadvantages:
- Not suitable for real-time control requiring fast data computation for slow measurement speeds
- Relatively wasteful of RAM
#define N 12char filter(){ int sum = 0; for(count = 0; count < N; count++) { sum += get_ad(); } return (char)(sum/N);}4. Recursive Average Filter Method
1. Method:
- Treat the continuous N sample values as a queue
- The length of the queue is fixed at N
- Each time a new data sample is taken, it is added to the end of the queue, and the data at the front of the queue is discarded (FIFO principle)
- Perform arithmetic averaging on the N data in the queue to obtain the new filtering result
- Selection of N: for flow, N=12; for pressure, N=4; for liquid level, N=4 ~ 12; for temperature, N=1 ~ 4
2. Advantages:
- Good suppression of periodic interference, high smoothness
- Suitable for systems with high-frequency oscillations
3. Disadvantages:
- Low sensitivity
- Poor suppression of sporadic pulse interference
- Difficult to eliminate sampling value deviations caused by pulse interference
- Not suitable for situations with severe pulse interference
- Relatively wasteful of RAM
/* A value is adjusted based on actual conditions, Value is the valid value, new_Value is the current sample value, the program returns the valid actual value */#define A 10char Value;char filter(){ char new_Value; new_Value = get_ad(); // Get sample value if( abs(new_Value - Value) > A) return Value; // abs() function to get absolute value return new_Value;}5. Median Average Filter Method
1. Method:
- Equivalent to “Median Filter Method” + “Arithmetic Mean Filter Method”
- Continuously sample N data, remove one maximum and one minimum value
- Then calculate the arithmetic mean of the N-2 data
- Selection of N: 3~14
2. Advantages:
- Combines the advantages of both filtering methods
- Can eliminate sampling value deviations caused by sporadic pulse interference
3. Disadvantages:
- Measurement speed is slower, similar to the arithmetic mean filter method
- Relatively wasteful of RAM
char filter(){ char count, i, j; char Value_buf[N]; int sum = 0; for(count = 0; count < N; count++) { Value_buf[count] = get_ad(); } 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; } } } for(count = 1; count < N-1; count ++) { sum += Value_buf[count]; } return (char)(sum/(N-2));}6. Clipping Average Filter Method
1. Method:
- Equivalent to “Clipping Filter Method” + “Recursive Average Filter Method”
- Each time a new data sample is taken, it is first subjected to clipping processing,
- Then sent to the queue for recursive average filtering
2. Advantages:
- Combines the advantages of both filtering methods
- Can eliminate sampling value deviations caused by sporadic pulse interference
3. Disadvantages:
- Relatively wasteful of RAM
#define A 10#define N 12char value, i = 0;char value_buf[N];char filter(){ char new_value, sum = 0; new_value = get_ad(); if(Abs(new_value - value) < A) value_buf[i++] = new_value; if(i==N) i=0; for(count = 0; count < N; count++) { sum += value_buf[count]; } return (char)(sum/N);}7. First-Order Lag Filter Method
1. Method:
- Take a=0~1
- The filtering result = (1-a) current sample value + a previous filtering result
2. Advantages:
- Good suppression of periodic interference
- Suitable for situations with high fluctuation frequencies
3. Disadvantages:
- Phase lag, low sensitivity
- The degree of lag depends on the value of a
- Cannot eliminate interference signals with frequencies higher than half the sampling frequency
/* To speed up program processing, take a=0~100 */#define a 30char value;char filter(){ char new_value; new_value = get_ad(); return ((100-a)*value + a*new_value);}8. Weighted Recursive Average Filter Method
1. Method:
- This is an improvement on the recursive average filter method, where data at different times is given different weights
- Typically, the closer the data is to the current time, the greater the weight.
- The greater the weight coefficient given to the new sample value, the higher the sensitivity, but the lower the signal smoothness
2. Advantages:
- Suitable for objects with large pure lag time constants
- And systems with short sampling periods
3. Disadvantages:
- For signals with small pure lag time constants and long sampling periods, changes are slow
- Cannot quickly respond to the severity of interference currently affecting the trading system, resulting in poor filtering effects
/* coe array is the weighting coefficient table */#define N 12char 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++) { value_buf[count] = get_ad(); } for(count = 0; count < N; count++) { sum += value_buf[count] * coe[count]; } return (char)(sum/sum_coe);}9. Debounce Filter Method
1. Method:
- Set a filtering counter
- Compare each sampled value with the current valid value:
- If the sampled value = current valid value, reset the counter
- If the sampled value > or < current valid value, increment the counter and check if the counter >= upper limit N (overflow)
- If the counter overflows, replace the current valid value with the current value and reset the counter
2. Advantages:
- Has a good filtering effect on slowly changing measured parameters,
- Can avoid the repeated on/off bouncing of the controller or the jitter of values on the display near the critical value
3. Disadvantages:
- Not suitable for rapidly changing parameters
- If the value sampled during the counter overflow happens to be an interference value, it will be treated as a valid value in the trading system
#define N 12char 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 Debounce Filter Method
1. Method:
- Equivalent to “Clipping Filter Method” + “Debounce Filter Method”
- First clip, then debounce
2. Advantages:
- Inherits the advantages of both “clipping” and “debounce”
- Improves certain defects in the “debounce filter method”, avoiding the introduction of interference values into the system
3. Disadvantages:
- Not suitable for rapidly changing parameters
#define A 10#define N 12char 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; }}