NAME

    Math::KalmanFilter - Kalman Filter(also known as Linear Quadratic
    Estimation) implementation for sensor fusion and such

VERSION

    version 0.06

SYNOPSIS

         use Math::KalmanFilter;
         use Time::HiRes qw(time);
     
         my $oldTime = time();
         # Read State from state sensor, in a IMU this would be one of the accelerometer orientation angle 
         # e.g. Angle between orientation vector and X axis in degrees.
         my $state = readStateSensor(); 
     
         # Read rate of change of state, in a IMU gyroscope measures the delta i.e. the Rate of change of
         # $state e.g. rate of change of angle between orientation vector and X axis in degree per second.
         my $delta = readDeltaSensor(); 
     
         #Created a Kalman filter object to hold state changes for your measurement target.
         $kalman = Math::KalmanFilter->new(
             angle => $state
         );
     
         while($keep_running){
             my $newTime = time();
             my $deltaTime = $newTime - $oldTime;
             $oldTime  = $newTime;
     
             my $state = readStateSensor(); 
             my $delta = readDeltaSensor(); 
             my $angle = $kalman->getAngle($state,$delta,$deltaTime);
     
             print "CURRENT ANGLE:$angle";
         }

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.

ATTRIBUTES

 qAngle

     * default: 0.001 

 qBias

     * default: 0.003

 rMeasure

     * default: 0.03

 bias

     * starting value(default): 0
     * recalculated(optimised) at each new sensor reading.

 covariance

    This is the covariance matrix, it is stored as a 2d array ref *
    starting value(default): [[0,0],[0,0]] * recalculated(optimised) at
    each new sensor reading.

 angle

    Calculated angle

METHODS

 getAngle

    Calculate new state based on observed reading from state sensor, delta
    sensor and time elapsed since last reading.

 BADGES

SUPPORT

 Bugs / Feature Requests

    Please report any bugs or feature requests through github at
    https://github.com/shantanubhadoria/perl-math-kalmanfilter/issues. You
    will be notified automatically of any progress on your issue.

 Source Code

    This is open source software. The code repository is available for
    public review and contribution under the terms of the license.

    https://github.com/shantanubhadoria/perl-math-kalmanfilter

      git clone git://github.com/shantanubhadoria/perl-math-kalmanfilter.git

AUTHOR

    Shantanu Bhadoria <shantanu at cpan dott org>

CONTRIBUTORS

      * Shantanu Bhadoria <shantanu.bhadoria@gmail.com>

      * Shantanu Bhadoria <shantanu@cpan.org>

COPYRIGHT AND LICENSE

    This software is copyright (c) 2016 by Shantanu Bhadoria.

    This is free software; you can redistribute it and/or modify it under
    the same terms as the Perl 5 programming language system itself.