224 lines
8.3 KiB
C++
224 lines
8.3 KiB
C++
#ifndef KLB_H
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#define KLB_H
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#include <chrono>
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#include <Indoor/math/divergence/KullbackLeibler.h>
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#include <Indoor/grid/factory/v2/GridFactory.h>
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#include <Indoor/floorplan/v2/Floorplan.h>
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#include <Indoor/floorplan/v2/FloorplanReader.h>
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#include <Indoor/grid/factory/v2/GridFactory.h>
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#include <Indoor/grid/factory/v2/Importance.h>
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#include <Indoor/geo/Heading.h>
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#include <Indoor/geo/Point2.h>
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#include <Indoor/sensors/offline/FileReader.h>
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#include <KLib/math/statistics/Statistics.h>
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#include <Indoor/sensors/imu/TurnDetection.h>
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#include <Indoor/sensors/imu/StepDetection.h>
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#include <Indoor/sensors/imu/MotionDetection.h>
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#include <Indoor/sensors/pressure/RelativePressure.h>
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#include <Indoor/sensors/radio/WiFiGridEstimator.h>
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#include <Indoor/sensors/radio/WiFiQualityAnalyzer.h>
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#include <Indoor/sensors/beacon/model/BeaconModelLogDistCeiling.h>
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#include <Indoor/math/MovingAVG.h>
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#include <Indoor/math/FixedFrequencyInterpolator.h>
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#include <Indoor/math/divergence/KullbackLeibler.h>
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#include <Indoor/math/divergence/JensenShannon.h>
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#include <Indoor/data/Timestamp.h>
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#include <KLib/math/filter/particles/Particle.h>
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#include <KLib/math/filter/particles/ParticleFilterMixing.h>
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#include <KLib/math/filter/particles/ParticleFilterInitializer.h>
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#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationWeightedAverage.h>
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#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationRegionalWeightedAverage.h>
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#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationOrderedWeightedAverage.h>
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//#include <KLib/math/filter/particles/estimation/ParticleFilterEstimationKernelDensity.h>
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#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingSimple.h>
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#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingPercent.h>
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#include <KLib/math/filter/particles/resampling/ParticleFilterResamplingDivergence.h>
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#include <KLib/math/filter/merging/MarkovTransitionProbability.h>
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#include <KLib/math/filter/merging/mixing/MixingSamplerDivergency.h>
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#include <KLib/math/filter/merging/estimation/JointEstimationPosteriorOnly.h>
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#include "Structs.h"
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#include "../Plotti.h"
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#include "Logic.h"
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#include "../Settings.h"
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double __KLD = 0.0;
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double __QUALITY = 0.0;
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//todo function return the transition prob matrix for markov chain!
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//getKernelDensityProbability should work fine for first shot! nevertheless we need to do 2 kernel density estimations for both filters :( :( :(
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struct ModeProbabilityTransition : public K::MarkovTransitionProbability<MyState, MyControl, MyObs>{
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Grid<MyNode>& grid;
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const double lambda;
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ModeProbabilityTransition(Grid<MyNode>& grid, double lambda) : grid(grid), lambda(lambda) {;}
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virtual Eigen::MatrixXd update(std::vector<K::ParticleFilterMixing<MyState, MyControl, MyObs>>& modes, const MyObs& obs) override {
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std::vector<double> probsWifiV;
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std::vector<double> probsParticleV;
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WiFiQualityAnalyzer analyzer;
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// mode[0] -> Posterior & mode[1] -> Wifi ---- i know what im doing :)
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for(MyNode node : grid.getNodes()){
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double probParzenPosterior = calcKernelDensity(node, modes[0].getParticles());
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probsParticleV.push_back(probParzenPosterior);
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double probParzenWifi = calcKernelDensity(node, modes[1].getParticles());
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probsWifiV.push_back(probParzenWifi);
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}
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// make vectors
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Eigen::Map<Eigen::VectorXd> probsWifi(&probsWifiV[0], probsWifiV.size());
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Eigen::Map<Eigen::VectorXd> probsParticle(&probsParticleV[0], probsParticleV.size());
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//calc wi-fi metrik
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const WiFiMeasurements wifiObs = Settings::WiFiModel::vg_eval.group(obs.wifi);
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if(!wifiObs.entries.empty()){
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analyzer.add(wifiObs);
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}
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float qualityWifi = analyzer.getQuality();
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if(std::isnan(qualityWifi)){
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qualityWifi = 1.0;
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} else if(qualityWifi == 0) {
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qualityWifi = 0.00000001;
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}
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// debugging global variable
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__QUALITY = qualityWifi;
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// get kld
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double kld = Divergence::KullbackLeibler<double>::getGeneralFromSamples(probsParticle, probsWifi, Divergence::LOGMODE::NATURALIS);
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// debugging global variable
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__KLD = kld;
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//exp. distribution
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double expKld = std::exp(-lambda * (kld * qualityWifi));
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Assert::isTrue(expKld < 1.0, "exp. distribution greater 1!");
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//create the matrix
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Eigen::MatrixXd m(2,2);
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m << expKld, 1.0 - expKld, 1 - qualityWifi, qualityWifi;
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return m;
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}
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double calcKernelDensity(const MyNode node, const std::vector<K::Particle<MyState>> particles){
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int size = particles.size();
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double prob = 0;
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#pragma omp parallel for reduction(+:prob) num_threads(6)
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for(int i = 0; i < size; ++i){
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double distance = particles[i].state.position.getDistanceInCM(node);
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prob += Distribution::Normal<double>::getProbability(0, 100, distance) * particles[i].weight;
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}
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return prob;
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}
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};
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struct ModeProbabilityTransitionNormal : public K::MarkovTransitionProbability<MyState, MyControl, MyObs>{
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const double lambda;
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WiFiQualityAnalyzer analyzer;
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//this is a hack! it is possible that the sigma of z is getting 0 and therefore the rank decreases to 2 and
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//no inverse matrix is possible
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Distribution::Uniform<float> uniRand = Distribution::Uniform<float>(-0.1, 0.1);
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/** ctor */
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ModeProbabilityTransitionNormal(double lambda) : lambda(lambda) {;}
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virtual Eigen::MatrixXd update(std::vector<K::ParticleFilterMixing<MyState, MyControl, MyObs>>& modes, const MyObs& obs) override {
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Assert::equal(modes[0].getParticles().size(), modes[1].getParticles().size(), "Particle.size() differs!");
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// create eigen matrix for posterior and wifi
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Eigen::MatrixXd mParticle(modes[0].getParticles().size(), 3);
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Eigen::MatrixXd mWifi(modes[1].getParticles().size(), 3);
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#pragma omp parallel for num_threads(6)
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for(int i = 0; i < modes[0].getParticles().size(); ++i){
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mParticle(i,0) = (modes[0].getParticles()[i].state.position.x_cm / 100.0) + uniRand.draw();
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mParticle(i,1) = (modes[0].getParticles()[i].state.position.y_cm / 100.0) + uniRand.draw();
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mParticle(i,2) = (modes[0].getParticles()[i].state.position.z_cm / 100.0) + uniRand.draw();
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mWifi(i,0) = (modes[1].getParticles()[i].state.position.x_cm / 100.0) + uniRand.draw();
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mWifi(i,1) = (modes[1].getParticles()[i].state.position.y_cm / 100.0) + uniRand.draw();
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mWifi(i,2) = (modes[1].getParticles()[i].state.position.z_cm / 100.0) + uniRand.draw();
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}
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// create normal distributions
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Eigen::VectorXd meanParticle(3);
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Point3 estParticle = modes[0].getEstimation().position.inMeter();
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meanParticle << estParticle.x, estParticle.y, estParticle.z;
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Distribution::NormalDistributionN normParticle = Distribution::NormalDistributionN::getNormalNFromSamplesAndMean(mParticle, meanParticle);
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Eigen::VectorXd meanWifi(3);
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Point3 estWifi = modes[1].getEstimation().position.inMeter();
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meanWifi << estWifi.x, estWifi.y, estWifi.z;
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Distribution::NormalDistributionN normWifi = Distribution::NormalDistributionN::getNormalNFromSamplesAndMean(mWifi, meanWifi);
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//calc wi-fi metrik
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const WiFiMeasurements wifiObs = Settings::WiFiModel::vg_eval.group(obs.wifi);
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if(!wifiObs.entries.empty()){
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analyzer.add(wifiObs);
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}
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float qualityWifi = analyzer.getQuality();
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if(std::isnan(qualityWifi)){
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qualityWifi = 1.0;
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} else if(qualityWifi == 0) {
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qualityWifi = 0.00000001;
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}
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// debugging global variable
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__QUALITY = qualityWifi;
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// get kld
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double kld = Divergence::KullbackLeibler<double>::getMultivariateGauss(normParticle, normWifi);
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// debugging global variable
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__KLD = kld;
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//exp. distribution
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double expKld = std::exp(-lambda * (kld * qualityWifi));
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Assert::isTrue(expKld < 1.0, "exp. distribution greater 1!");
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//create the matrix
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Eigen::MatrixXd m(2,2);
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//m << expKld, 1.0 - expKld, 1 - qualityWifi, qualityWifi;
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m << 1, 0, 0, 1.0;
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return m;
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}
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};
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#endif // KLB_H
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