File perl-Math-KalmanFilter.spec of Package perl-Math-KalmanFilter

Overview Repositories Revisions Requests Users Attributes Meta

File perl-Math-KalmanFilter.spec of Package perl-Math-KalmanFilter (Revision 241e641f74ef2e4c5ece7601f1b80a70)

Currently displaying revision 241e641f74ef2e4c5ece7601f1b80a70 , Show latest

#
# spec file for package perl-Math-KalmanFilter
#
# Copyright (c) 2016 SUSE LINUX GmbH, Nuernberg, Germany.
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.

# Please submit bugfixes or comments via http://bugs.opensuse.org/
#

Name:           perl-Math-KalmanFilter
Version:        0.06
Release:        0
%define cpan_name Math-KalmanFilter
Summary:        Kalman Filter(also known as Linear Quadratic Estimation) implementation [cut]
License:        Artistic-1.0 or GPL-1.0+
Group:          Development/Libraries/Perl
Url:            http://search.cpan.org/dist/Math-KalmanFilter/
Source0:        http://www.cpan.org/authors/id/S/SH/SHANTANU/%{cpan_name}-%{version}.tar.gz
BuildArch:      noarch
BuildRoot:      %{_tmppath}/%{name}-%{version}-build
BuildRequires:  perl
BuildRequires:  perl-macros
BuildRequires:  perl(Moose)
Requires:       perl(Moose)
%{perl_requires}

%description
The Kalman filter, also known as linear quadratic estimation (LQE), is an
algorithm that uses a series of measurements observed over time, containing
noise (random variations) and other inaccuracies, and produces estimates of
unknown variables that tend to be more precise than those based on a single
measurement alone.

Algorithm is recursive, which means it takes the output of its previous
calculations as a factor in calculating the next step which improves its
accuracy over time. The key to Kalman filters are two sensors with
different kind of accuracy issues in each. Sensor A or the state sensor
might give in-accurate value for a measurement on the whole but it doesn't
drift. Sensor B or delta sensor gives gives much more accurate rate of
change in value(or delta) but it drifts over time due to its small
inaccuracies as it only measures rate of change in value and not the actual
value. Kalman filter uses this knowledge to fuse results from both sensors
to give a state value which is more accurate than state value received from
any of these filters alone.

An example of application for this is calculating orientation of objects
using Gyroscopes and Accelerometers.

While Accelerometer is usually used to measure gravity it can be used to
measure the inclination of a body with respect to the surface of earth
along the x and y axis(not z axis as Z axis is usually facing the opposite
direction as the force of gravity) by measuring the direction in which the
force of gravity is applied.

Gyroscope measures the rate of rotation about one or all the axis of a
body. while it gives fairly accurate estimation of the angular velocity, if
we use it to calculate the current inclination based on the starting
inclination and the angular velocity, there is a lot of drift, which means
the gyroscope error will accumulate over time as we calculate newer angles
based on previous angle and angular velocity and the error in angular
velocity piles on.

A real life example of how Kalman filter works is while driving on a
highway in a car. If you take the time passed since when your started
driving and your estimated average speed every hour and use it to calculate
the distance you have traveled your calculation will become more inaccurate
as you drive on.

This is drift in value. However if you watch each milestone and calculate
your current position using milestone data and your speed since the last
milestone your result will be much more accurate. That is approximately
close

to how Kalman filter works.

%prep
%setup -q -n %{cpan_name}-%{version}

%build
%{__perl} Makefile.PL INSTALLDIRS=vendor
%{__make} %{?_smp_mflags}

%check
%{__make} test

%install
%perl_make_install
%perl_process_packlist
%perl_gen_filelist

%files -f %{name}.files
%defattr(-,root,root,755)
%doc Changes LICENSE README

%changelog

Places

File perl-Math-KalmanFilter.spec of Package perl-Math-KalmanFilter (Revision 241e641f74ef2e4c5ece7601f1b80a70)

Places