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// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include <dlib/filtering.h>
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <dlib/matrix.h>
#include <dlib/rand.h>
#include "tester.h"
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
logger dlog("test.filtering");
// ----------------------------------------------------------------------------------------
template <typename filter_type>
double test_filter (
filter_type kf,
int size
)
{
// This test has a point moving in a circle around the origin. The point
// also gets a random bump in a random direction at each time step.
running_stats<double> rs;
dlib::rand rnd;
int count = 0;
const dlib::vector<double,3> z(0,0,1);
dlib::vector<double,2> p(10,10), temp;
for (int i = 0; i < size; ++i)
{
// move the point around in a circle
p += z.cross(p).normalize()/0.5;
// randomly drop measurements
if (rnd.get_random_double() < 0.7 || count < 4)
{
// make a random bump
dlib::vector<double,2> pp;
pp.x() = rnd.get_random_gaussian()/3;
pp.y() = rnd.get_random_gaussian()/3;
++count;
kf.update(p+pp);
}
else
{
kf.update();
dlog << LTRACE << "MISSED MEASUREMENT";
}
// figure out the next position
temp = (p+z.cross(p).normalize()/0.5);
const double error = length(temp - rowm(kf.get_predicted_next_state(),range(0,1)));
rs.add(error);
dlog << LTRACE << temp << "("<< error << "): " << trans(kf.get_predicted_next_state());
// test the serialization a few times.
if (count < 10)
{
ostringstream sout;
serialize(kf, sout);
istringstream sin(sout.str());
filter_type temp;
deserialize(temp, sin);
kf = temp;
}
}
return rs.mean();
}
// ----------------------------------------------------------------------------------------
void test_kalman_filter()
{
matrix<double,2,2> R;
R = 0.3, 0,
0, 0.3;
// the variables in the state are
// x,y, x velocity, y velocity, x acceleration, and y acceleration
matrix<double,6,6> A;
A = 1, 0, 1, 0, 0, 0,
0, 1, 0, 1, 0, 0,
0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1;
// the measurements only tell us the positions
matrix<double,2,6> H;
H = 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0;
kalman_filter<6,2> kf;
kf.set_measurement_noise(R);
matrix<double> pn = 0.01*identity_matrix<double,6>();
kf.set_process_noise(pn);
kf.set_observation_model(H);
kf.set_transition_model(A);
DLIB_TEST(equal(kf.get_observation_model() , H));
DLIB_TEST(equal(kf.get_transition_model() , A));
DLIB_TEST(equal(kf.get_measurement_noise() , R));
DLIB_TEST(equal(kf.get_process_noise() , pn));
DLIB_TEST(equal(kf.get_current_estimation_error_covariance() , identity_matrix(pn)));
double kf_error = test_filter(kf, 300);
dlog << LINFO << "kf error: "<< kf_error;
DLIB_TEST_MSG(kf_error < 0.75, kf_error);
}
// ----------------------------------------------------------------------------------------
void test_rls_filter()
{
rls_filter rls(10, 0.99, 0.1);
DLIB_TEST(rls.get_window_size() == 10);
DLIB_TEST(rls.get_forget_factor() == 0.99);
DLIB_TEST(rls.get_c() == 0.1);
double rls_error = test_filter(rls, 1000);
dlog << LINFO << "rls error: "<< rls_error;
DLIB_TEST_MSG(rls_error < 0.75, rls_error);
}
// ----------------------------------------------------------------------------------------
class filtering_tester : public tester
{
public:
filtering_tester (
) :
tester ("test_filtering",
"Runs tests on the filtering stuff (rls and kalman filters).")
{}
void perform_test (
)
{
test_rls_filter();
test_kalman_filter();
}
} a;
}
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