Multiple Heterogeneous Sensor Tasking Optimization in the Absence of Measurement Feedback

Observations of resident space objects generated by sensors are the primary method of maintaining knowledge of the object states. With the increasing number of objects, efficient sensor allocation is becoming integral. This requires the coordination of multiple sensors with different capabilities in an optimized manner. While the optimization is greatly simplified if instantaneous communication between the sensors can be assumed and immediate processing is available, this is not a realistic setup. Information exchange and processing induces time delays that are longer than the time available to plan and start the sensor tasking step, without unnecessarily idling the sensor. In this paper, a method is introduced to form efficient sensor tasking in a multi-sensor system, without immediate communication between the sensors and observation processing, in the following called feedback. The coordination is exemplified using two sensors, with different fields of view in a follow-up scenario of objects in the geosynchronous region. The efficiency of the method is evaluated using the two line element catalog.

[1]  Marcelo G. S. Bruno Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering , 2013, Synthesis Lectures on Signal Processing.

[2]  Aubrey B. Poore,et al.  Multiple Hypothesis Tracking (MHT) for Space Surveillance: Results and Simulation Studies , 2013 .

[3]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[4]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[5]  Roberto Furfaro,et al.  An Autonomous Sensor Tasking Approach for Large Scale Space Object Cataloging , 2017 .

[6]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[7]  Carolin Frueh,et al.  Heuristic and Optimized Sensor Tasking Observation Strategies with Exemplification for Geosynchronous Objects , 2018 .

[8]  Koki Ho,et al.  Information-theoretic target search for space situational awareness , 2018 .

[9]  Carolin Frueh,et al.  Multiple-Object Space Surveillance Tracking Using Finite-Set Statistics , 2015 .

[10]  R S Erwin,et al.  Dynamic sensor tasking for Space Situational Awareness , 2010, Proceedings of the 2010 American Control Conference.

[11]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[12]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Weston R. Faber,et al.  Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST) , 2016, ArXiv.

[14]  Carolin Frueh,et al.  Novel multi-object filtering approach for space situational awareness , 2018 .

[15]  R. Mahler Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[16]  Carolin Frueh,et al.  Determining characteristics of artificial near-Earth objects using observability analysis , 2018 .

[17]  D. Vallado Fundamentals of Astrodynamics and Applications , 1997 .

[18]  Carolin Frueh,et al.  Space Situational Awareness Sensor Tasking: Comparison of Machine Learning with Classical Optimization Methods , 2020 .

[19]  Suman Chakravorty,et al.  Information Space Receding Horizon Control for Multisensor Tasking Problems , 2016, IEEE Transactions on Cybernetics.

[20]  Carolin Frueh,et al.  Noise estimation and probability of detection in non-resolved images: Application to space object observation , 2019, Advances in Space Research.