158 lines
5.2 KiB
C++
158 lines
5.2 KiB
C++
#ifndef MYSMOOTHINGTRANSITIONEXPERIMENTAL_H
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#define MYSMOOTHINGTRANSITIONEXPERIMENTAL_H
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#include <KLib/math/filter/particles/ParticleFilterTransition.h>
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#include <KLib/math/filter/smoothing/BackwardFilterTransition.h>
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#include <KLib/math/distribution/Normal.h>
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#include <KLib/math/distribution/Uniform.h>
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#include <Indoor/geo/Angle.h>
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#include "../MyState.h"
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#include "../MyControl.h"
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#include "../../Helper.h"
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#include "../../toni/barometric.h"
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class MySmoothingTransitionExperimental : public K::BackwardFilterTransition<MyState> {
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private:
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/** a simple normal distribution */
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K::NormalDistribution distWalk;
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public:
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/**
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* ctor
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* @param choice the choice to use for randomly drawing nodes
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* @param fp the underlying floorplan
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*/
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MySmoothingTransitionExperimental() :
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distWalk(smoothing_walk_mu, smoothing_walk_sigma)
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{
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distWalk.setSeed(4321);
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}
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public:
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uint64_t ts = 0;
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uint64_t deltaMS = 0;
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/** set the current time in millisconds */
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void setCurrentTime(const uint64_t ts) {
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if (this->ts == 0) {
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this->ts = ts;
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deltaMS = 0;
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} else {
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deltaMS = this->ts - ts;
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this->ts = ts;
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}
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}
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/**
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* smoothing transition starting at T with t, t-1,...0
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* @param particles_new p_t (Forward Filter) p2
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* @param particles_old p_t+1 (Smoothed Particles from Step before) p1
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* q(p1 | p2) is calculated
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*/
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std::vector<std::vector<double>> transition(std::vector<K::Particle<MyState>>const& particles_new,
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std::vector<K::Particle<MyState>>const& particles_old ) override {
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// calculate alpha(m,n) = p(q_t+1(m) | q_t(n))
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// this means, predict all possible states q_t+1 with all passible states q_t
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// e.g. p(q_490(1)|q_489(1));p(q_490(1)|q_489(2)) ... p(q_490(1)|q_489(N)) and
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// p(q_490(1)|q_489(1)); p(q_490(2)|q_489(1)) ... p(q_490(M)|q_489(1))
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std::vector<std::vector<double>> predictionProbabilities;
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omp_set_dynamic(0); // Explicitly disable dynamic teams
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omp_set_num_threads(4);
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#pragma omp parallel for shared(predictionProbabilities)
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for (int i = 0; i < particles_old.size(); ++i) {
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std::vector<double> innerVector;
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auto p1 = &particles_old[i];
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for(int j = 0; j < particles_new.size(); ++j){
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auto p2 = &particles_new[j];
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const double distance_m = p2->state.pCur.getDistance(p1->state.pCur) / 100.0;
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double muDistance = 1.0;
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double sigmaDistance = 0.5;
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switch (p2->state.currentActivity) {
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case Activity::ELEVATOR:
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muDistance = 0.0;
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sigmaDistance = 0.3;
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break;
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case Activity::STAIRS_DOWN:
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muDistance = 0.5;
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sigmaDistance = 0.3;
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break;
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case Activity::STAIRS_UP:
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muDistance = 0.4;
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sigmaDistance = 0.2;
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break;
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case Activity::STANDING:
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muDistance = 0.0;
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sigmaDistance = 0.2;
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break;
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case Activity::WALKING:
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muDistance = 1.0;
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sigmaDistance = 0.5;
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break;
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default:
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muDistance = 1.0;
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sigmaDistance = 0.5;
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break;
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}
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const double distProb = K::NormalDistribution::getProbability(muDistance, sigmaDistance, distance_m);
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// is the heading change similiar to the measurement?
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double diffRad = Angle::getDiffRAD_2PI_PI(p2->state.walkState.heading.getRAD(), p1->state.walkState.heading.getRAD());
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double diffDeg = Angle::radToDeg(diffRad);
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double angularChangeZeroToPi = std::fmod(std::abs(p1->state.angularHeadingChange), 360.0);
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const double headingProb = K::NormalDistribution::getProbability(angularChangeZeroToPi, smoothing_heading_sigma, diffDeg);
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//check how near we are to the measurement
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const double floorProb = K::NormalDistribution::getProbability(p1->state.measurement_pressure, smoothing_baro_sigma, p2->state.hPa);
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//combine the probabilities
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double prob = distProb * headingProb * floorProb;
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innerVector.push_back(prob);
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//error checks
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// if(distance_m != distance_m) {throw "detected NaN";}
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// if(distProb != distProb) {throw "detected NaN";}
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// if(headingProb != headingProb) {throw "detected NaN";}
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// if(floorProb != floorProb) {throw "detected NaN";}
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// if(floorProb == 0) {throw "detected zero prob in floorprob";}
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// if(prob != prob) {throw "detected NaN";}
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//if(prob == 0) {++zeroCounter;}
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}
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#pragma omp critical
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predictionProbabilities.push_back(innerVector);
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}
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return predictionProbabilities;
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}
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};
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#endif // MYTRANSITION_H
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