priyatharsan / orbdetpy

A Python library for orbit determination

orbdetpy - Orbit determination in Python and Java

Introduction

This is orbdetpy, a library of Python and Java routines for orbit determination. It is a thin Python wrapper for our estimation tools and Orekit, which are both written in Java.

orbdetpy is free software, distributed under the terms of the GNU General Public License.

Features

The force model for orbit propagation currently includes:

1. EGM96 gravity field up to degree and order 360.
2. Earth solid tides due to the influence of the Sun and Moon.
3. FES 2004 ocean tide model up to degree and order 100.
4. The NRL MSISE-00 and simple exponential models for atmospheric drag.
5. Solar radiation pressure.
6. Third body perturbations from the Sun and Moon.

The measurement model supports range, range-rate, angles, and inertial Cartesian coordinates. Filtering is done using Orekit's Extended Kalman Filter or our custom Unscented Kalman Filter. Dynamic Model Compensation (DMC) can be used with either filter to estimate additional perturbing acclerations that result from unmodeled dynamics, maneuvers etc.

You can either use your own measurements or simulate observations using the simulateMeasurements() function.

Installation

1. Python 3.6+ must be installed with the packages numpy, scipy, pyjnius, and matplotlib.
2. Install the Java Development Kit 8+ (1.8+) from here. Set the JAVA_HOME environment variable to point to your JDK installation.

The lib/ folder contains JAR files for the following libraries, which are imported by orbdetpy automatically.

1. Google gson
2. Hipparchus 1.4+
3. Orekit 9.3+

Space weather data in data/ is obtained from CelesTrak.

Examples

The following example programs can be found in the 'examples' folder. These examples use the Python wrapper interface but calling the underlying Java implementation directly is quite straightforward.

1. testsim.py : Demonstrates the measurement simulator. Note that maneuvers can be incorporated into the force model during simulation.
2. plotsim.py : Plots the results of simulations created using testsim.py.
3. testodet.py : Demonstrates orbit determination in orbdetpy.
4. plotodet.py : Plots the results of fitting orbits using testodet.py.

orbdetpy uses JSON files to store settings, measurements and estimation results. The files in examples/data show how to configure measurement simulation and orbit determination using radar or telescope data. The file docs/filefmt.rst documents the structure of the JSON files.

The following are some typical use cases. It is assumed that the current working directory is examples/data.

1. Simulate state vectors and radar measurements:

   python ../testsim.py radar_sim_cfg.json sim_data.json

   This will run the simulation configured in radar_sim_cfg.json and write simulated output to sim_data.json.

2. Plot simulation results:

   python ../plotsim.py radar_sim_cfg.json sim_data.json

   This will plot the simulated data generated in (1).

3. Run OD on simulated radar data:

   python ../testodet.py radar_od_cfg.json sim_data.json od_output.json

   This will run OD on the simulated radar data generated in (1) using the OD configuration in radar_od_cfg.json and write OD output to od_output.json.

4. Plot OD results:

   python ../plotodet.py radar_od_cfg.json sim_data.json od_output.json

   This will plot the OD results from (3).

Future Work

The following tasks are under consideration. Community contributions are always welcome.

1. A batch least squares implementation.
2. Rauch-Tung-Striebel smoother.
3. Parametric analysis i.e. the ability to pass-through certain measurement types.

Bug Reports

Comments, criticisms and bug reports are very welcome and may be sent to the package maintainer by email or the project's website.

Shiva Iyer <shiva.iyer AT utexas DOT edu>