Reduce noise in refclock sample dispersions
Use the estimated dispersion only if it's higher than long-term average. This should improve performance with short polling intervals.
This commit is contained in:
Miroslav Lichvar
parent
97f2f16fd6
commit
b9b0326d15
3 changed files with 85 additions and 8 deletions
64
refclock.c
64
refclock.c
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@ -53,6 +53,8 @@ struct MedianFilter {
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int index;
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int used;
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int last;
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int avg_var_n;
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double avg_var;
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struct FilterSample *samples;
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int *selected;
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double *x_data;
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@ -101,6 +103,7 @@ static void log_sample(RCL_Instance instance, struct timeval *sample_time, int f
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static void filter_init(struct MedianFilter *filter, int length);
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static void filter_fini(struct MedianFilter *filter);
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static void filter_reset(struct MedianFilter *filter);
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static double filter_get_avg_sample_dispersion(struct MedianFilter *filter);
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static void filter_add_sample(struct MedianFilter *filter, struct timeval *sample_time, double offset, double dispersion);
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static int filter_get_last_sample(struct MedianFilter *filter, struct timeval *sample_time, double *offset, double *dispersion);
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static int filter_select_samples(struct MedianFilter *filter);
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@ -348,7 +351,7 @@ RCL_AddSample(RCL_Instance instance, struct timeval *sample_time, double offset,
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LCL_GetOffsetCorrection(sample_time, &correction, &dispersion);
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UTI_AddDoubleToTimeval(sample_time, correction, &cooked_time);
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dispersion += LCL_GetSysPrecisionAsQuantum();
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dispersion += LCL_GetSysPrecisionAsQuantum() + filter_get_avg_sample_dispersion(&instance->filter);
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if (!valid_sample_time(instance, sample_time))
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return 0;
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@ -374,7 +377,7 @@ RCL_AddPulse(RCL_Instance instance, struct timeval *pulse_time, double second)
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LCL_GetOffsetCorrection(pulse_time, &correction, &dispersion);
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UTI_AddDoubleToTimeval(pulse_time, correction, &cooked_time);
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dispersion += LCL_GetSysPrecisionAsQuantum();
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dispersion += LCL_GetSysPrecisionAsQuantum() + filter_get_avg_sample_dispersion(&instance->filter);
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if (!valid_sample_time(instance, pulse_time))
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return 0;
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@ -526,7 +529,6 @@ poll_timeout(void *arg)
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int sample_ok, stratum;
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sample_ok = filter_get_sample(&inst->filter, &sample_time, &offset, &dispersion);
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filter_reset(&inst->filter);
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inst->driver_polled = 0;
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if (sample_ok) {
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@ -634,6 +636,9 @@ filter_init(struct MedianFilter *filter, int length)
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filter->index = -1;
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filter->used = 0;
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filter->last = -1;
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/* set first estimate to system precision */
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filter->avg_var_n = 0;
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filter->avg_var = LCL_GetSysPrecisionAsQuantum() * LCL_GetSysPrecisionAsQuantum();
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filter->samples = MallocArray(struct FilterSample, filter->length);
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filter->selected = MallocArray(int, filter->length);
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filter->x_data = MallocArray(double, filter->length);
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@ -658,6 +663,12 @@ filter_reset(struct MedianFilter *filter)
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filter->used = 0;
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}
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static double
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filter_get_avg_sample_dispersion(struct MedianFilter *filter)
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{
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return sqrt(filter->avg_var);
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}
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static void
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filter_add_sample(struct MedianFilter *filter, struct timeval *sample_time, double offset, double dispersion)
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{
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@ -789,8 +800,8 @@ static int
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filter_get_sample(struct MedianFilter *filter, struct timeval *sample_time, double *offset, double *dispersion)
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{
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struct FilterSample *s, *ls;
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int i, n;
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double x, y, d, e;
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int i, n, dof;
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double x, y, d, e, var, prev_avg_var;
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n = filter_select_samples(filter);
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@ -818,6 +829,8 @@ filter_get_sample(struct MedianFilter *filter, struct timeval *sample_time, doub
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y /= n;
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e /= n;
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e -= sqrt(filter->avg_var);
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if (n >= 4) {
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double b0, b1, s2, sb0, sb1;
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@ -829,18 +842,53 @@ filter_get_sample(struct MedianFilter *filter, struct timeval *sample_time, doub
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as dispersion */
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RGR_WeightedRegression(filter->x_data, filter->y_data, filter->w_data, n,
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&b0, &b1, &s2, &sb0, &sb1);
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var = s2;
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d = sb0;
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dof = n - 2;
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} else if (n >= 2) {
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for (i = 0, d = 0.0; i < n; i++)
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d += (filter->y_data[i] - y) * (filter->y_data[i] - y);
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d = sqrt(d / (n - 1));
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var = d / (n - 1);
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d = sqrt(var);
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dof = n - 1;
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} else {
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d = 0.0;
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var = filter->avg_var;
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d = sqrt(var);
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dof = 1;
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}
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/* avoid having zero dispersion */
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if (var < 1e-20) {
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var = 1e-20;
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d = sqrt(var);
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}
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prev_avg_var = filter->avg_var;
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/* update exponential moving average of the variance */
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if (filter->avg_var_n > 100) {
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filter->avg_var += dof / (dof + 100.0) * (var - filter->avg_var);
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} else {
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filter->avg_var = (filter->avg_var * filter->avg_var_n + var * dof) /
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(dof + filter->avg_var_n);
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if (filter->avg_var_n == 0)
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prev_avg_var = filter->avg_var;
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filter->avg_var_n += dof;
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}
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/* reduce noise in sourcestats weights by using the long-term average
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instead of the estimated variance if it's not significantly lower */
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if (var * dof / RGR_GetChi2Coef(dof) < prev_avg_var)
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d = sqrt(filter->avg_var) * d / sqrt(var);
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if (d < e)
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d = e;
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UTI_AddDoubleToTimeval(&ls->sample_time, x, sample_time);
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*offset = y;
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*dispersion = d + e;
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*dispersion = d;
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filter_reset(filter);
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return 1;
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}
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24
regress.c
24
regress.c
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@ -135,6 +135,30 @@ RGR_GetTCoef(int dof)
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}
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}
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/* ================================================== */
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/* Get 90% quantile of chi-square distribution */
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double
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RGR_GetChi2Coef(int dof)
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{
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static double coefs[] = {
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2.706, 4.605, 6.251, 7.779, 9.236, 10.645, 12.017, 13.362,
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14.684, 15.987, 17.275, 18.549, 19.812, 21.064, 22.307, 23.542,
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24.769, 25.989, 27.204, 28.412, 29.615, 30.813, 32.007, 33.196,
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34.382, 35.563, 36.741, 37.916, 39.087, 40.256, 41.422, 42.585,
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43.745, 44.903, 46.059, 47.212, 48.363, 49.513, 50.660, 51.805,
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52.949, 54.090, 55.230, 56.369, 57.505, 58.641, 59.774, 60.907,
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62.038, 63.167, 64.295, 65.422, 66.548, 67.673, 68.796, 69.919,
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71.040, 72.160, 73.279, 74.397, 75.514, 76.630, 77.745, 78.860
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};
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if (dof <= 64) {
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return coefs[dof-1];
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} else {
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return 1.2 * dof; /* Until I can be bothered to do something better */
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}
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}
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/* ================================================== */
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/* Structure used for holding results of each regression */
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@ -61,6 +61,11 @@ RGR_WeightedRegression
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extern double RGR_GetTCoef(int dof);
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/* Return the value to apply to the variance to make an upper one-sided
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test assuming a chi-square distribution. */
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extern double RGR_GetChi2Coef(int dof);
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/* Return a status indicating whether there were enough points to
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carry out the regression */
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