WIP: JIPDA example

[sglvladi]

As discussed during the user conference earlier, this PR is created to demonstrate an example implementation of JIPDA.

The implemented JIPDA is an implementation of the equations specified in Section 3 of [1]. Note that no consideration of track-birth is taken, since this is handled separately by the PHD filter described in Section 4 of the same paper.

A relation between JPDA and JIPDA is also drawn in Section 4.4 of [2], where I also investigated how we can use JPDA (as well as algorithms such as EHM) as a black box to obtain an implementation of JIPDA. This is also the approach used in the implementation shown in this PR.

Some notes:

• The added components assume that Tracks have an "exist_prob" property. This is added as a dynamic property at runtime (see here).
• A JIPDA data associator has been implemented. It's operation is identical to JPDA, except from the addition of lines 210-212 and 238-239.
• An IPDA hypothesiser has been created that performs prediction of the existence variable:
  • Overall, its operation is identical to the PDA hypothesiser, except from the addition of lines 218-222 and a slight modification of the probability calculation in line 262.
  • The hypothesiser also pre-caclculates the value of a weight w, required to adjust the null hypothesis weight computed by JIPDA (see here). An explanation of why this is necessary can be found in Section 4.4.5 of [2].
• An additional metadata property has been added to SingleHypothesis to enable us to communicate the value of w to JIPDA.
• An example script has also been included (see examples/jipda_example.py)

References:
[1] Horridge, Paul & Maskell, Simon & Ltd, Qinetiq. (2011). Using a Probabilistic Hypothesis Density filter to confirm tracks in a multi-target environment.
[2] Vladimirov, Lyudmil (2021) Mathematical Models and Monte-Carlo Algorithms for Improved Detection of Targets in the Commercial Maritime Domain. Doctor of Philosophy thesis, University of Liverpool.