Michael Bynum, Sandia National Laboratories; Forest Danford, Sandia National Laboratories; Georgia Stinchfield, Carnegie Mellon University; Carl Laird, Carnegie Mellon University; Cody Karcher, California State University, Long Beach
Keywords: Sensor placement optimization, mixed-integer programming
Abstract:
Optimal selection and placement of terrestrial space situational awareness remote sensing systems is critical for effective utilization of valuable assets to satisfy mission requirements. This problem is extremely challenging when selecting from a set of heterogeneous sensors within realistic budget under scenarios where the number of active targets exceeds the number of sensors. Scalable sensor placement algorithms are well established for the case where all sensors are static (Legg, et al., 2012); however, optimizing the sensor placement problem is far more challenging when both stationary and dynamic sensors are candidates. Our exemplar considers both stationary and gimbaled sensors, which requires incorporating scheduling constraints (e.g., slew times) within the sensor placement problem to properly evaluate the merits of each type of sensor. The resulting problem is a large-scale mixed-integer linear program (MILP) that is intractable for full-scale problems of interest. We present a temporal decomposition algorithm for efficiently and optimally selecting both where to place sensors and the type of sensor(s) that should be selected at each location given a budget in order to satisfy mission criteria. The decomposition algorithm exploits problem structure induced by the temporal aspects of the scheduling constraints to solve a set of significantly smaller MILPs (in parallel) within a custom branch and bound (B&B) algorithm. This approach is broadly extensible based on the formulation chosen and the B&B algorithm is guaranteed to converge to the optimal solution.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525
Bibliography
Legg, S., Benavides-Serrano, A., Siirola, J., Watson, J.-P., Davis, S., Bratteteig, A., & Laird, C. (2012). A stochastic programming approach for gas detector placement using CFD-based dispersion simulations. Computers and Chemical Enigneering, 194-201.
Date of Conference: September 17-20, 2024
Track: Space Domain Awareness