#define GRAVITY 9.80665f
/* 720 LSG = 1G */
-#define LSG (1024.0f)
-#define NUMOFACCDATA (8.0f)
+#define LSG (1024.0)
+#define NUMOFACCDATA (8.0)
/* conversion of acceleration data to SI units (m/s^2) */
#define CONVERT_A (GRAVITY_EARTH / LSG / NUMOFACCDATA)
#define CONVERT_A_Z(x) ((float(x)/1000) * (GRAVITY * 1.0))
/* conversion of magnetic data to uT units */
-#define CONVERT_M (1.0f/6.6f)
+#define CONVERT_M (1.0/6.6)
#define CONVERT_M_X (-CONVERT_M)
#define CONVERT_M_Y (-CONVERT_M)
#define CONVERT_M_Z (CONVERT_M)
-#define CONVERT_GAUSS_TO_MICROTESLA(x) ( (x) * 100 )
+#define CONVERT_GAUSS_TO_MICROTESLA(x) ((x) * 100 )
/* conversion of orientation data to degree units */
-#define CONVERT_O (1.0f/64.0f)
+#define CONVERT_O (1.0/64)
#define CONVERT_O_A (CONVERT_O)
#define CONVERT_O_P (CONVERT_O)
#define CONVERT_O_R (-CONVERT_O)
/*conversion of gyro data to SI units (radian/sec) */
-#define CONVERT_GYRO ((2000.0f/32767.0f)*((float)M_PI / 180.0f))
+#define CONVERT_GYRO (2000.0/32767*M_PI/180)
#define CONVERT_GYRO_X (-CONVERT_GYRO)
#define CONVERT_GYRO_Y (-CONVERT_GYRO)
#define CONVERT_GYRO_Z (CONVERT_GYRO)
}
}
-// Platform sensor orientation
+/* Platform sensor orientation */
#define DEF_ORIENT_ACCEL_X -1
#define DEF_ORIENT_ACCEL_Y -1
#define DEF_ORIENT_ACCEL_Z -1
#define DEF_ORIENT_GYRO_Y 1
#define DEF_ORIENT_GYRO_Z 1
-// G to m/s2
+/* G to m/s2 */
#define CONVERT_FROM_VTF16(s,d,x) (convert_from_vtf_format(s,d,x))
#define CONVERT_A_G_VTF16E14_X(s,d,x) (DEF_ORIENT_ACCEL_X *\
convert_from_vtf_format(s,d,x)*GRAVITY)
#define CONVERT_A_G_VTF16E14_Z(s,d,x) (DEF_ORIENT_ACCEL_Z *\
convert_from_vtf_format(s,d,x)*GRAVITY)
-// Degree/sec to radian/sec
+/* Degree/sec to radian/sec */
#define CONVERT_G_D_VTF16E14_X(s,d,x) (DEF_ORIENT_GYRO_X *\
convert_from_vtf_format(s,d,x) * \
- ((float)M_PI/180.0f))
+ M_PI/180)
#define CONVERT_G_D_VTF16E14_Y(s,d,x) (DEF_ORIENT_GYRO_Y *\
convert_from_vtf_format(s,d,x) * \
- ((float)M_PI/180.0f))
+ M_PI/180)
#define CONVERT_G_D_VTF16E14_Z(s,d,x) (DEF_ORIENT_GYRO_Z *\
convert_from_vtf_format(s,d,x) * \
- ((float)M_PI/180.0f))
+ M_PI/180)
-// Milli gauss to micro tesla
+/* Milli gauss to micro tesla */
#define CONVERT_M_MG_VTF16E14_X(s,d,x) (convert_from_vtf_format(s,d,x)/10)
#define CONVERT_M_MG_VTF16E14_Y(s,d,x) (convert_from_vtf_format(s,d,x)/10)
#define CONVERT_M_MG_VTF16E14_Z(s,d,x) (convert_from_vtf_format(s,d,x)/10)
}
-static void denoise (struct sensor_info_t* si, struct sensors_event_t* data,
- int num_fields, int max_samples)
-{
- /*
- * Smooth out incoming data using a moving average over a number of
- * samples. We accumulate one second worth of samples, or max_samples,
- * depending on which is lower.
- */
-
- int i;
- int f;
- int sampling_rate = (int) si->sampling_rate;
- int history_size;
- int history_full = 0;
-
- /* Don't denoise anything if we have less than two samples per second */
- if (sampling_rate < 2)
- return;
-
- /* Restrict window size to the min of sampling_rate and max_samples */
- if (sampling_rate > max_samples)
- history_size = max_samples;
- else
- history_size = sampling_rate;
-
- /* Reset history if we're operating on an incorrect window size */
- if (si->history_size != history_size) {
- si->history_size = history_size;
- si->history_entries = 0;
- si->history_index = 0;
- si->history = (float*) realloc(si->history,
- si->history_size * num_fields * sizeof(float));
- if (si->history) {
- si->history_sum = (float*) realloc(si->history_sum,
- num_fields * sizeof(float));
- if (si->history_sum)
- memset(si->history_sum, 0, num_fields * sizeof(float));
- }
- }
-
- if (!si->history || !si->history_sum)
- return; /* Unlikely, but still... */
-
- /* Update initialized samples count */
- if (si->history_entries < si->history_size)
- si->history_entries++;
- else
- history_full = 1;
-
- /* Record new sample and calculate the moving sum */
- for (f=0; f < num_fields; f++) {
- /**
- * A field is going to be overwritten if
- * history is full, so decrease the history sum
- */
- if (history_full)
- si->history_sum[f] -=
- si->history[si->history_index * num_fields + f];
-
- si->history[si->history_index * num_fields + f] = data->data[f];
- si->history_sum[f] += data->data[f];
-
- /* For now simply compute a mobile mean for each field */
- /* and output filtered data */
- data->data[f] = si->history_sum[f] / si->history_entries;
- }
-
- /* Update our rolling index (next evicted cell) */
- si->history_index = (si->history_index + 1) % si->history_size;
-}
-
-
static void clamp_gyro_readings_to_zero (int s, struct sensors_event_t* data)
{
float x, y, z;
/* Always consider the accelerometer accurate */
data->acceleration.status = SENSOR_STATUS_ACCURACY_HIGH;
if (sensor_info[s].quirks & QUIRK_NOISY)
- denoise(&sensor_info[s], data, 3, 20);
+ denoise(s, data);
break;
case SENSOR_TYPE_MAGNETIC_FIELD:
- calibrate_compass (data, &sensor_info[s], get_timestamp());
+ calibrate_compass (data, &sensor_info[s]);
if (sensor_info[s].quirks & QUIRK_NOISY)
- denoise(&sensor_info[s], data, 3, 100);
+ denoise(s, data);
break;
case SENSOR_TYPE_GYROSCOPE:
sensor_info[s].motion_trigger_name)
calibrate_gyro(data, &sensor_info[s]);
- /* For noisy sensors we'll drop a very few number
- * of samples to make sure we have at least MIN_SAMPLES events
- * in the filtering queue. This is to make sure we are not sending
- * events that can disturb our mean or stddev.
+ /*
+ * For noisy sensors drop a few samples to make sure we
+ * have at least GYRO_MIN_SAMPLES events in the
+ * filtering queue. This improves mean and std dev.
*/
if (sensor_info[s].quirks & QUIRK_NOISY) {
- denoise_median(&sensor_info[s], data, 3);
- if((sensor_info[s].selected_trigger !=
- sensor_info[s].motion_trigger_name) &&
- sensor_info[s].event_count < MIN_SAMPLES)
+ if (sensor_info[s].selected_trigger !=
+ sensor_info[s].motion_trigger_name &&
+ sensor_info[s].event_count<GYRO_MIN_SAMPLES)
return 0;
+
+ denoise(s, data);
}
/* Clamp near zero moves to (0,0,0) if appropriate */
break;
}
- /* Add this event to our global records, for filtering purposes */
- record_sample(s, data);
-
return 1; /* Return sample to Android */
}
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