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Passive Location Resource Scheduling Based on an Improved Genetic Algorithm

National Digital Switching System Engineering and Technological Research Center (NDSC), Zhengzhou 450000, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2093;
Received: 5 May 2018 / Revised: 23 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
(This article belongs to the Collection Positioning and Navigation)
PDF [11528 KB, uploaded 29 June 2018]


With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision. View Full-Text
Keywords: passive location; NP-hard; scheduling; genetic algorithm; angle-of-arrival passive location; NP-hard; scheduling; genetic algorithm; angle-of-arrival

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Jiang, J.; Zhang, J.; Zhang, L.; Ran, X.; Tang, Y. Passive Location Resource Scheduling Based on an Improved Genetic Algorithm. Sensors 2018, 18, 2093.

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