Evolutionary sensor allocation for the Space Surveillance Network

Abstract

The congested exosphere continues to contain more satellites and debris, raising the potential for destructive collisions. The Special Perturbations (SP) Tasker algorithm currently assigns the ground sensors tasks to track object locations. Accurate locations help avoid collisions. However, the SP Tasker ignores priority, which is the satellite's importance factor. This article introduces the Evolutionary Algorithm Tasker (EAT) to solve the Satellite Sensor Allocation Problem (SSAP), which is a hybrid Evolutionary Strategy and Genetic Algorithm concept including specific techniques to explore the solution space and exploit the best solutions found. This approach goes beyond the current method, which does not include priority and other methods from the literature that have been applied to small-scale simulations. The SSAP model implementation extends Multi-Objective Evolutionary Algorithms (MOEAs) from the literature while accounting for priorities. Multiple real-world factors are considered, including each sensor's field-of-view, the orbital opportunities to track a satellite, the capacity of the sensor, and the relative priority of the satellites. The single objective EAT is statistically compared to the SP Tasker algorithm. Simulations show that both the EAT and MOEA approaches effectively use priority in the core tasking algorithms to ensure that higher priority satellites are tracked.

Space surveillance architecture: issues and challenges – an integrated approach

“…Of course, the SSAP (Satellite Sensor Allocation Problem) can be approached by novel techniques based on algorithms such as the proposed EAT (Evolutionary Algorithm Tasker) and MOEA (Multi-Objective Evolutionary Algorithm), which are focused on priority to ensure the tracking of higher priority satellites. 16…”

A content analysis-based approach to explore simulation verification and identify its current challenges

“…For example, verifying human interactions involves checking increasing orders of magnitudes of socially and spatially oriented interactions [30,34] and increasing quantities of agents and options for modeling decision making [138]. Verifying safety-critical systems deals with identifying issues pertaining to multiple internal layers for self-monitoring, safety checks for maintaining flight paths and altitude [77,139], and collision avoidance [140]. Epstein [141] develops a mathematical model incorporating, among other factors, social components in order to explore the emergent dynamics of network structure.…”

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

“…For the allocation problem, each individual sensor is considered as an agent with particular capabilities [ 16 ]. Sensor allocation has been previously achieved using different methods, such as a network flow optimization model and cause effect graph [ 4 , 17 ], data association algorithm in sensor network [ 18 ], market-based approaches applied in multi agent systems [ 19 , 20 , 21 , 22 , 23 ], and dynamic allocation and coalition-based approach [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”