27 April 2010 A comparison of stochastic optimizers applied to dynamic sensor scheduling for satellite tracking
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Abstract
This paper presents a comparison of stochastic optimizers running inside a centralized sensor resource manager (SRM) for scheduling the tasks (observations) of an ensemble of space observing kinematic sensors. The manager is designed to operate as a receding horizon controller in a closed feedback loop with a linear filter based multiple hypothesis tracker (MHT) that fuses the disparate sensor data to produce target declarations and state estimates. The reward function is based on expected entropic information gain of satellite tracks over the planning horizon. A comparison between several stochastic optimizers, namely: particle swarm optimizers (PSO), evolutionary algorithms (EA), and the simultaneous perturbation and stochastic approximation (SPSA) algorithm is performed over the resulting high dimensional, Markovian, and discontinuous reward function. The algorithms were evaluated by simulating space surveillance scenarios using idealized optical sensors, satellite two-line element (TLE) sets from the US Space Track catalog, and relevant factors such as line of sight visibility. Simulation results show a hybrid PSO and EA algorithm outperforms the other algorithms over the tests performed.
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Andrew J. Newman, Andrew J. Newman, Sean R. Martin, Sean R. Martin, Benjamin M. Rodriguez, Benjamin M. Rodriguez, Nishant L. Mehta, Nishant L. Mehta, Eric M. Klatt, Eric M. Klatt, } "A comparison of stochastic optimizers applied to dynamic sensor scheduling for satellite tracking", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970M (27 April 2010); doi: 10.1117/12.850204; https://doi.org/10.1117/12.850204
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