data_validator.cpp

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/**
 * @file data_validator.c
 *
 * A data validation class to identify anomalies in data streams
 *
 * @author Lorenz Meier <lorenz@px4.io>
 */

#include "data_validator.h"

#include <ecl.h>

void
DataValidator::put(uint64_t timestamp, float val, uint64_t error_count_in, int priority_in)
{
    float data[dimensions] = { val }; //sets the first value and all others to 0

    put(timestamp, data, error_count_in, priority_in);
}

void
DataValidator::put(uint64_t timestamp, const float val[dimensions], uint64_t error_count_in, int priority_in)
{
    _event_count++;

    if (error_count_in > _error_count) {
        _error_density += (error_count_in - _error_count);

    } else if (_error_density > 0) {
        _error_density--;
    }

    _error_count = error_count_in;
    _priority = priority_in;

    for (unsigned i = 0; i < dimensions; i++) {
        if (_time_last == 0) {
            _mean[i] = 0;
            _lp[i] = val[i];
            _M2[i] = 0;

        } else {
            float lp_val = val[i] - _lp[i];

            float delta_val = lp_val - _mean[i];
            _mean[i] += delta_val / _event_count;
            _M2[i] += delta_val * (lp_val - _mean[i]);
            _rms[i] = sqrtf(_M2[i] / (_event_count - 1));

            if (fabsf(_value[i] - val[i]) < 0.000001f) {
                _value_equal_count++;

            } else {
                _value_equal_count = 0;
            }
        }

        _vibe[i] = _vibe[i] * 0.99f + 0.01f * fabsf(val[i] - _lp[i]);

        // XXX replace with better filter, make it auto-tune to update rate
        _lp[i] = _lp[i] * 0.99f + 0.01f * val[i];

        _value[i] = val[i];
    }

    _time_last = timestamp;
}

float
DataValidator::confidence(uint64_t timestamp)
{
    float ret = 1.0f;

    /* check if we have any data */
    if (_time_last == 0) {
        _error_mask |= ERROR_FLAG_NO_DATA;
        ret = 0.0f;

    } else if (timestamp - _time_last > _timeout_interval) {
        /* timed out - that's it */
        _error_mask |= ERROR_FLAG_TIMEOUT;
        ret = 0.0f;

    } else if (_value_equal_count > _value_equal_count_threshold) {
        /* we got the exact same sensor value N times in a row */
        _error_mask |= ERROR_FLAG_STALE_DATA;
        ret = 0.0f;

    } else if (_error_count > NORETURN_ERRCOUNT) {
        /* check error count limit */
        _error_mask |= ERROR_FLAG_HIGH_ERRCOUNT;
        ret = 0.0f;

    } else if (_error_density > ERROR_DENSITY_WINDOW) {
        /* cap error density counter at window size */
        _error_mask |= ERROR_FLAG_HIGH_ERRDENSITY;
        _error_density = ERROR_DENSITY_WINDOW;

    }

    /* no critical errors */
    if (ret > 0.0f) {
        /* return local error density for last N measurements */
        ret = 1.0f - (_error_density / ERROR_DENSITY_WINDOW);

        if (ret > 0.0f) {
            _error_mask = ERROR_FLAG_NO_ERROR;
        }
    }

    return ret;
}

void
DataValidator::print()
{
    if (_time_last == 0) {
        ECL_INFO("\tno data");
        return;
    }

    for (unsigned i = 0; i < dimensions; i++) {
        ECL_INFO("\tval: %8.4f, lp: %8.4f mean dev: %8.4f RMS: %8.4f conf: %8.4f",
             (double) _value[i], (double)_lp[i], (double)_mean[i],
             (double)_rms[i], (double)confidence(ecl_absolute_time()));
    }
}