70 lines
2.2 KiB
C++
70 lines
2.2 KiB
C++
#include "Eval.h"
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#include "Settings.h"
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#include <Indoor/math/distribution/Normal.h>
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double ftmEval(SensorMode UseSensor, const Timestamp& currentTime, const Point3& particlePos, const std::vector<WiFiMeasurement>& measurements, std::shared_ptr<std::unordered_map<MACAddress, Kalman>> ftmKalmanFilters)
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{
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double result = 1.0;
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for (WiFiMeasurement wifi : measurements)
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{
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if (wifi.getNumSuccessfulMeasurements() < 3)
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{
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continue;
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}
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const MACAddress& mac = wifi.getAP().getMAC();
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int nucIndex = Settings::nucIndex(mac);
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const Point3 apPos = Settings::data.CurrentPath.nucInfo(nucIndex).position;
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// particlePos.z = 1.3; // smartphone höhe
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const float apDist = particlePos.getDistance(apPos);
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// compute ftm distance
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float ftm_offset = Settings::data.CurrentPath.NUCs.at(mac).ftm_offset;
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float ftmDist = wifi.getFtmDist() + ftm_offset; // in m; plus static offset
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float rssi_pathloss = Settings::data.CurrentPath.NUCs.at(mac).rssi_pathloss;
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float rssiDist = LogDistanceModel::rssiToDistance(-40, rssi_pathloss, wifi.getRSSI());
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if (UseSensor == SensorMode::FTM)
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{
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if (ftmDist > 0)
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{
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//double sigma = wifi.getFtmDistStd()*wifi.getFtmDistStd(); // 3.5; // TODO
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double sigma = 5;
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if (ftmKalmanFilters != nullptr)
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{
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Kalman& kalman = ftmKalmanFilters->at(mac);
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ftmDist = kalman.predict(currentTime, ftmDist);
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//sigma = std::sqrt(kalman.P(0, 0));
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Assert::isTrue(sigma > 0, "sigma");
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double x = Distribution::Normal<double>::getProbability(ftmDist, sigma, apDist);
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result *= x;
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}
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else
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{
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double x = Distribution::Normal<double>::getProbability(ftmDist, sigma, apDist);
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result *= x;
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}
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}
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}
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else
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{
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// RSSI
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double sigma = 5;
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double x = Distribution::Normal<double>::getProbability(rssiDist, sigma, apDist);
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result *= x;
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}
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}
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return result;
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} |