Digital filtering is often used in AD acquisition, and there are different filtering requirements in different situations. The following are the programs and advantages and disadvantages of 10 classic software filtering methods:
1. Limiting filtering method (also known as program judgment filtering method)
2, the median value filtering method
3. Arithmetic average filtering method
4, recursive average filtering method (also known as sliding average filtering method)
5, the median average filtering method (also known as anti-pulse interference average filtering method)
6. Limiting average filtering method
7. First-order lag filtering method
8. Weighted recursive average filtering method
9, debounce filtering method
10. Limiting debounce filtering method
1, limited secondary filteringA, method:
According to experience, determine the maximum deviation allowed for two samplings (set to A)
Determine each time a new value is detected:
If the difference between this value and the previous value is "=A, then this value is valid.
If the difference between this value and the previous value is "A", the current value is invalid, the current value is discarded, and the current value is used instead of the current value.
B, advantages:
Can effectively overcome the pulse interference caused by accidental factors
C, shortcomings
Unable to suppress that kind of periodic interference
Poor smoothness
program:
/* A value can be adjusted according to actual conditions
Value is a valid value, new_value is the current sample value
The filter returns a valid actual value*/
#define A 10
Char value;
Char filter()
{
Char new_value;
New_value = get_ad();
If ( ( new_value - value 》 A ) || ( value - new_value 》 A ) )
Return value;
Else
Return new_value;
}
2, the median value filtering methodA, method:
Continuously sample N times (N takes an odd number), arrange the N sample values ​​by size, and take the middle value as the current effective value.
B, advantages:
It can effectively overcome the fluctuation interference caused by accidental factors, and has a good filtering effect on the measured parameters with slow changes in temperature and liquid level.
C, disadvantages:
It is not suitable for parameters such as flow rate and speed that change rapidly.
program:
/* N value can be adjusted according to the actual situation
Sorting using bubbling method*/
#define N 11
Char filter()
{
Charvalue_buf[N];
Char count,i,j,temp;
For ( count=0;count"N;count++)
{
Value_buf[count] = get_ad();
Delay();
}
For(j=0;j"N-1;j++)
{
For(i=0;i"Nj-1;i++)
{
If ( value_buf "value_buf[i+1] )
{
Temp =value_buf;
Value_buf = value_buf[i+1];
Value_buf[i+1] = temp;
}
}
}
Returnvalue_buf[(N-1)/2];
}
3. Arithmetic average filtering methodA, method:
Continuously take N sample values ​​for arithmetic average operation
When the value of N is large: the signal smoothness is high, but the sensitivity is low.
When the value of N is small: the signal smoothness is low, but the sensitivity is high.
Selection of N value: general flow, N=12; pressure: N=4
B, advantages:
Suitable for filtering signals that generally have random interference
The characteristic of such a signal is that there is an average value, and the signal fluctuates around a certain range of values.
C, disadvantages:
Not applicable to real-time control with slower measurement speed or faster data calculation speed
Worried RAM
program:
#define N 12
Char filter()
{
Int sum = 0;
For ( count=0;count"N;count++)
{
Sum + =get_ad();
Delay();
}
Return (char)(sum/N);
}
4, recursive average filtering method (also known as sliding average filtering method) (FIR predecessor)A, method:
Treat N consecutive samples as a queue
The length of the queue is fixed to N
Each time a new data is sampled, it is put into the end of the team, and the data of the original team leader is discarded. (first in, first out principle)
By performing arithmetic averaging on the N data in the queue, a new filtering result can be obtained.
Selection of N value: flow rate, N=12; pressure: N=4; liquid level, N=4~12; temperature, N=1~4
B, advantages:
Good suppression of periodic interference, high smoothness
System for high frequency oscillation
C, disadvantages:
Low sensitivity
Poor inhibition of occasional impulsive interference
It is not easy to eliminate the deviation of sample values ​​caused by pulse interference
Not suitable for occasions where pulse interference is serious
Worried RAM
program:
#define N 12
Char value_buf[N];
Char i=0;
Char filter()
{
Char count;
Int sum=0;
Value_buf[i++] = get_ad();
If ( i == N ) i = 0;
For ( count=0;count "N,count++"
Sum+ = value_buf[count];
Return (char)(sum/N);
}
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