Note : MORTICIA is in the very early stages of development. The following description is based on the desired result.
MORTICIA is Monte-carlo Optical Rendering for Theatre Investigations of Capability under the Influence of the Atmosphere,
an open-source optical remote sensing and surveillance modelling framework written mainly in Python.
The theatre of operations is a geographical region defined by a polygon in which an optical remote sensing or surveillance system are deployed or to be deployed. Besides the engineering characteristics of the optical remote sensing or surveillance system, the atmosphere plays a key role in determining system effectiveness. In the case of a surveillance system, effectiveness may be quantified in terms of the range at which targets can be Detected, Recognised or Identified (DRI).
Some of the key capabilities of MORTICIA are:
- Physically accurate rendering of targets of interest in atmospheric conditions that are statistically representative of the theatre of operations.
- Statistically valid Monte-carlo simulations of the Detect, Recognition and Identification (DRI) ranges of such targets in the theatre of operations.
The climatology within the theatre must be taken into account in order to obtain statistically valid results. Some of the most important elements are:
- Cloud climatology
- Aerosol climatology
- Surface reflectance climatology (e.g. vegetation state)
- Atmospheric turbulence climatology
The atmospheric turbulence climatology is especially important for high resolution, long range optical surveillance systems deployed within the Atmospheric Boundary Layer (ABL). High resolution space sensors are impacted less by turbulence in general.
In addition, there are certain fixed geometrical aspects relevant to surveillance that are defined by selection of the theatre. These include solar/lunar azimuth and elevation statistics and surface topography.
Typical output of MORTICIA takes the form of a database of results that define target appearance and DRI, having chosen
a sensor position and a target position within the theatre at a particular time of day and day of year. The atmospheric
conditions can be chosen explicitly or generated randomly on the basis of the theatre climatology.
The Python package pandas is the preferred environment for mining the results.
MORTICIA tools include utilities for building
- theatre boundaries and climatologies,
- theatre topography
- target geometry and optical characteristics
- sensor characteristics
- radiant environment maps (using the libRadtran radiative transfer code)
Code documentation for MORTICIA is generated using Sphinx.
MORTICIA has been developed largely in Python 2.7 and has not yet been tested in Python 3.X.
A working installation of libRadtran is required to compute radiant environment
maps and atmospheric transmittance. In the Monte carlo/statistical mode of operation, a compute cluster
is generally required to achieve adequate sampling in a reasonable time. Parallel computation is performed
using the ipyparallel package, which works in
IPython/Jupyter notebooks as well as other Python launch modes.
MORTICIA has been developed using the Anaconda distribution from
Continuum Analytics and this is the recommended distribution for MORTICIA users.
In principle, any Python 2.7 installation that can meet the dependencies should also work.
The master repository for MORTICIA is publicly hosted on GitHub at
https://github.com/derekjgriffith/MORTICIA.