A multi-sensor adaptive management method for cooperative detection of multi-UAV is proposed

Abstract

The invention relates to a multi-sensor adaptive management method for multi-UAV cooperative detection. The method includes target cooperative recognition model based on Bayesian network reasoning technology. Obtaining The cooperative perception probability of multi-sensor feature level by off-line learning and on-line learning based on Bayesian network. Adopoting Perceptual information entropy tocharacterize the uncertainty of the target category. Based on perceptual information entropy, establishing the detection closed loop of perceptual-learning-decision-making-action from target cooperative recognition to sensor dynamic management i. Using perceptual information entropy increment as the judging criterion of sensor resource dynamic scheduling in multi-UAV cooperative detection network; proposing An intelligent optimization algorithm based on genetic algorithm and particle swarm optimization algorithm to improve the allocation efficiency of multi-UAV-multi-objective. From the viewpoint of multi-sensor and multi-target feature recognition, the unknown target is recognized by using the multi-features detected by multi-sensors.

Description

technical field [0001] The invention belongs to the technical field of multi-UAV cooperative detection, in particular to a multi-sensor self-adaptive management method for multi-UAV cooperative detection. Background technique [0002] With the rapid development of science and technology, UAV has become a research hotspot at home and abroad. As a kind of unmanned aerial vehicle that can be controlled and can perform multiple tasks, UAV has a wide range of application prospects in both military and civilian applications. In the future, swarms will be the development trend of drones. [0003] In the process of cluster information sharing, how to transmit the perceived target and platform status information to other individuals, so that the whole system can not only meet the available bandwidth limit to reduce the probability of being detected, but also meet the requirements of collaborative control and decision-making need is a very important question. In the multi-UAV coope...

AI Technical Summary

Problems solved by technology

Most of these technologies perform sensor optimization management on the basis of comprehensive target recognition at the decision-making level, and cannot fully utilize the maximum effectiveness of different detection characteristics of various types of sensors; in addition, due to the complexity of the battlefield environment, two sensors may be used against the same target. Making an opposite judgment may cause the failure of the sensor adaptive scheduling algorithm based on D-S evidence theory

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