Orbital Recognition System for Space

Debris Tracking:

A Journey from Inner-Brain GPS to

Outer-Space GPS

Amber Yang

Keplerian Elements and Kinematic

Vectors

Orbital Patterns within Keplerian

Elements

Orbital Patterns within Keplerian

Elements

Are there inherent geometrical patterns in the orbits ofspace debris that can be learned by an Artificial NeuralNetwork for accurate detection and tracking over time?

Question

Orbital Recognition ANN System Plot

Diagram

Parameters of the Orbital Recognition

ANN System

Table 1: Parameters of ANN-Based Orbital Recognition System Input

Layer Hidden Layers

/Neurons

Output Layer

Training Rate

Weighting Momentum

MSE

Samples

Target Detection

ANN

5

3 / 75

3

0.5

0.11

2.00E-3

1000

Retraining

Detection ANN

5

3/75

3

0.33

0.33

2.78E-5 Retrained

Cases

Trajectory

Prediction ANN

25

2 / 10

5

0.4

0.33

1.00E-5

1

Sensitivity Analyses for Target

Detection ANN

Target Detection ANN Simulation

Orbital Recognition ANN System Plot

Diagram

Simulation Results: Trajectory

Prediction ANN

Figure 1: Simulation of training waypoints at every 1-degree true anomaly and tracking from 0-degree true anomaly

Figure 2: Simulation of training waypoints at every 1-degree true anomaly and tracking shifted 45-degree true anomaly

Figure 3: Simulation of training waypoints at every 0.2-degree true anomaly and tracking from 0-degree true anomaly

Figure 4: Simulation of training waypoints at every 0.2-degree true anomaly and tracking shifted 45-degree true anomaly

Trajectory Prediction ANN Simulation

Comprehensive Collision Avoidance

Developed from Orbital Recognition System

[1] B. G. Cour-Palais and D. J. Kessler, “Collision Frequency of Artificial Satellites: The Creation of a Debris Belt,”

Journal of Geophysical Research: Space Physics., June 1, 1978, vol. 83, pp. 2637-2646.

[2] D. J. Kessler, N. L. Johnson, J. –C. Liou, and M. Matney, “The Kessler Syndrome: Implications to Future Space

Operations,” Advances in Astronautical Sciences, Feb. 6-10, 2010, vol. 137, pp. 47-61.

[3] D. A. Vallado and S. S. Carter, “Accurate Orbit Determination from Short-Arc Dense Observational Data,” Journal of

the Astronautical Sciences., April 1998, vol. 46, pp. 195-213

[4] O’Keefe et. al., “The Hippocampus As a Cognitive Map,” vol. 3. Oxford: Clarendon Press, 1978.

[5] K. Hill, “Maneuver Detection and Estimation with Optical Tracklets,” In Proceedings of the Advanced Maui Optical

and Space Surveillance Technologies Conference., Sept. 9-12 2014.

[6] N. Y. Xiao, “Using the Modified Back-propagation Algorithm To Perform Automated Downlink Analysis,” Department

of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology., June 1, 1996, pp. 23-31.

[7] P. J. Werbos, “Backpropagation Through Time: What It Does and How To Do It,” In Proceedings of IEEE., Oct. 1990,

vol. 78, no. 10, pp. 1550-1554.

[8] P. Payeur, H. Le-Huy, and C. M. Gosselin, “Trajectory Prediction for Moving Objects Using Artificial Neural

Networks,” IEEE Transactions on Industrial Electronics., April 1995, vol. 42 no. 2, pp. 147-150.

[9] S. T. Aghav and S. A. Gangal, “Simplified Orbit Determination Algorithm for Low Earth Orbit Satellites Using

Spaceborne GPS Navigation Sensor,” Artificial Satellites., June 2014, vol. 49, no. 2, pp. 81-99.

[10] Y. L. Fablec and J. M. Alliot, “Using Neural Networks to Predict Aircraft Trajectories,” In Proceedings of the

International Conference on Artificial Intelligence., 1999.

References