Scheduled Imaging of Multiple Threat Aircraft Using a Modified Traveling Salesman Problem

Kang, Changkoo; Woolsey, Craig A.

doi:10.2514/1.i010871

Cited by 1 publication

(3 citation statements)

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“…As mentioned above, the observation scheduling problem is divided into three subproblems based on our proposed method. These sub-problems can be formulated by maximum coverage problem (MCP) and variants of vehicle routing problem (VRP), which have been proved to be NP-hard [15,17]. To solve these combinatorial problems, it is commonly believed that exact algorithms cannot find optimal solutions within an acceptable time.…”

Section: Related Workmentioning

confidence: 99%

“…The task clustering problem can be treated as a MCP [15] that aims to cover all ground targets by using the minimum number of field of views (FOV). Since the result of the sensor scheduling is the observation sequence of ground targets and the cruise path of the airship is formed by stagnation nodes, the sensor scheduling and cruise path planning problems can be regarded as two variants of VRP [17,18]. Thus, the original scheduling problem can be decomposed and then solved in a step-by-step manner.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The FOV is defined as a circular area on the ground in which targets can be observed by slewing the imaging sensor [16]. As to the sensor scheduling and cruising path planning problems, since the aim of them is to find permutations of nodes, they can be formulated as variants of vehicle routing problems (VRP) [17,18].…”

Section: Introductionmentioning

confidence: 99%

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An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

Chen

Luo

2022

Sensors

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Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.

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Section: Related Workmentioning

confidence: 99%