kazu
06e0e0a5aa
added corresponding test-cases switched to newer version of tinyxml due to some issues adjusted affected code-parts accordingly for better re-use, moved ceiling-calculation to a new class some minor fixes new helper methods worked on wifi-opt
84 lines
2.7 KiB
C++
84 lines
2.7 KiB
C++
#ifdef WITH_TESTS
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#include "../../Tests.h"
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#include "../../../sensors/radio/setup/WiFiOptimizerLogDistCeiling.h"
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#include "../../../sensors/radio/setup/WiFiFingerprint.h"
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#include "../../../misc/Debug.h"
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#include <random>
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/**
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* test the wifi-optimizer by generating synthetic fingerprints and optimizing parameters from them
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*/
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TEST(WiFiOptimizer, optimize) {
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const VAPGrouper vg(VAPGrouper::Mode::DISABLED, VAPGrouper::Aggregation::AVERAGE);
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const MACAddress mac1("00:00:00:00:00:01");
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const MACAddress mac2("00:00:00:00:00:02");
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const Point3 pos1(10, 12, 5);
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const Point3 pos2(20, -10, 2);
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// building with one floor at 3.0 meters
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Floorplan::IndoorMap map;
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Floorplan::Floor floor1; floor1.atHeight = 3.0f;
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Floorplan::FloorOutlinePolygon poly1; poly1.poly.points.push_back(Point2(-30, -30)); poly1.poly.points.push_back(Point2(+30, +30));
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floor1.outline.push_back(&poly1);
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map.floors.push_back(&floor1);
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// add the two APs to the model
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WiFiModelLogDistCeiling mdl(&map);
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mdl.addAP(mac1, WiFiModelLogDistCeiling::APEntry(pos1, -40, 2, -4));
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mdl.addAP(mac2, WiFiModelLogDistCeiling::APEntry(pos2, -40, 2, -4));
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// generate some (synthetic) fingerprints
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std::minstd_rand gen;
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std::vector<WiFiFingerprint> fingerprints;
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for (int i = 0; i < 50; ++i) {
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std::uniform_real_distribution<float> distX(-30, +30);
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std::uniform_real_distribution<float> distY(-30, +30);
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std::uniform_real_distribution<float> distZ( -9, +9);
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// get a random position and calculate the model RSSIs
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const Point3 randomPt(distX(gen), distY(gen), distZ(gen));
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const float rssi1 = mdl.getRSSI(mac1, randomPt);
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const float rssi2 = mdl.getRSSI(mac2, randomPt);
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// construct a corresponding synthetic fingerprint
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WiFiFingerprint fp(randomPt);
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fp.measurements.entries.push_back(WiFiMeasurement(AccessPoint(mac1), rssi1));
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fp.measurements.entries.push_back(WiFiMeasurement(AccessPoint(mac2), rssi2));
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fingerprints.push_back(fp);
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}
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WiFiOptimizer::LogDistCeiling opt(&map, vg);
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for (const WiFiFingerprint& fp : fingerprints) {
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opt.addFingerprint(fp);
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}
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ASSERT_EQ(2, opt.getAllMACs().size());
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WiFiOptimizer::LogDistCeiling::Stats errRes;
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const WiFiOptimizer::LogDistCeiling::APParams params1 = opt.optimize(mac1, errRes);
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ASSERT_TRUE(errRes.error_db < 0.1);
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ASSERT_NEAR(0, pos1.getDistance(params1.getPos()), 0.4); // apx position estimation
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ASSERT_NEAR(-40, params1.txp, 1.0);
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ASSERT_NEAR(2, params1.exp, 0.1);
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ASSERT_NEAR(-4, params1.waf, 0.5);
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const WiFiOptimizer::LogDistCeiling::APParams params2 = opt.optimize(mac2, errRes);
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ASSERT_TRUE(errRes.error_db < 0.1);
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ASSERT_NEAR(0, pos2.getDistance(params2.getPos()), 0.4); // apx position estimation
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ASSERT_NEAR(-40, params1.txp, 1.0);
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ASSERT_NEAR(2, params2.exp, 0.1);
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ASSERT_NEAR(-4, params2.waf, 0.5);
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}
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#endif
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