A. the median average filter method (also known as the average pulse interference filter method)

B. method: it is equal to “median filter method” + “arithmetic average filter”. Take N continuous data, get rid of a minimum and a maximum value, and then calculate the arithmetic mean of N-2 data. N value select: 3~14

C. advantages: blend the advantages of two filtering methods, it can eliminate impulse interference caused by sampling bias for the occasional pulse interference.

D. disadvantages: measured speed is slow and it is wasting RAM the same as arithmetic average filter.

E. test code

int Filter_Value;

void setup () {
Serial.begin (9600); / / initialize the serial communication
randomSeed (analogRead (0)); / / random seed
}

void loop () {
Filter_Value = Filter (); / / get the filter output value
Serial.println (Filter_Value); / / serial output
delay (50);
}

/ / Used to randomly generate a current value of around 300
int Get_AD () {
return random (295, 305);
}

/ / Median average filtering method ( also known as anti- pulse interference average filtering method ) ( Algorithm 1 )
# define FILTER_N 100
int Filter () {
int i, j;
int filter_temp, filter_sum = 0;
int filter_buf [FILTER_N];
for (i = 0; i <FILTER_N; i + +) {
filter_buf [i] = Get_AD ();
delay (1);
}
/ / Sample values ​​in ascending order ( Bubble Act )
for (j = 0; j <FILTER_N - 1; j + +) {
for (i = 0; i <FILTER_N - 1 - j; i + +) {
if (filter_buf [i]> filter_buf [i + 1]) {
filter_temp = filter_buf [i];
filter_buf [i] = filter_buf [i + 1];
filter_buf [i + 1] = filter_temp;
}
}
}
Averaging / / remove the maximum and minimum extremes
for (i = 1; i <FILTER_N - 1; i + +) filter_sum + = filter_buf [i];
return filter_sum / (FILTER_N - 2);
}