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In target tracking mission planning, one of the difficulties is to generate Multi-sensor mission planning under the guidance of resources information. This paper proposes a SVM-based target tracking multi-sensor mission planning method. The method firstly generates a mission-planning database as a training set based on typical mission scenarios. Then, the hierarchical learning model was established by SVM algorithm, and the sensor-target allocation and sensor resource allocation were carried out respectively. After the model was trained by the scheme database, the intelligent generation of mission planning scheme can be realized. So that the feasibility and robustness of the method are verified by simulation
Inspec keywords: resource allocation; support vector machines; target tracking; learning (artificial intelligence); sensor fusion
Subjects: Radar equipment, systems and applications; Geophysics computing; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Aerospace control; Signal processing theory; Computer vision and image processing techniques; Mobile robots; Image recognition; Military circuits, components, and equipment; Other topics in statistics; Optimisation techniques; Signal processing and detection; Optimisation techniques