Abstract
In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of “predict re-predict” is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with “measurement and then update” and “predict and update” mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing.
Highlights
Artificial intelligence technology is currently at the forefront of academic innovation and more and more widely used in target detection and tracking [1, 2]
Most of the establishment of dynamic alliance is based on serial structure [16,17,18,19,20]. e main problem of this structure is low efficiency and easy-to-cause resource waste or excessive strain. e formation of multisensor dynamic alliance is mainly divided into two parts: formation and connection. e essence of multisensor alliance formation belongs to multisensor multitarget assignment problem
According to the hybrid dynamic alliance model, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DPSO) with sensitive particles is designed and the effectiveness of swarm intelligence algorithm is verified by simulation analysis compared with the existing literature
Summary
Artificial intelligence technology is currently at the forefront of academic innovation and more and more widely used in target detection and tracking [1, 2]. Chen et al [32] proposed particle swarm optimization (PSO) to solve the problem of alliance formation, but the algorithm did not consider the dynamic changes of the environment. The application of multisensor cross-cueing technology in swarm intelligence is proposed to solve the formation and connection problem of multisensor hybrid dynamic alliance. According to the hybrid dynamic alliance model, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DPSO) with sensitive particles is designed and the effectiveness of swarm intelligence algorithm is verified by simulation analysis compared with the existing literature. We propose the application of improved swarm intelligence algorithm (DDPSO) in multisensor cross-cueing technology.
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