Unmanned aerial vehicle target detection method based on multi-sensor information fusion

Abstract

The invention relates to an unmanned aerial vehicle target detection method based on multi-sensor information fusion. The method comprises the steps: step 1, carrying out time and coordinate registration on radar and photoelectric equipment, monitoring a low-altitude protection area in real time to obtain feature information of a small target, and carrying out feature layer fusion on the feature information; step 2, collecting and expanding images of various unmanned aerial vehicle targets to serve as an unmanned aerial vehicle target detection data set, and introducing an SSD deep learning network for training to obtain an SSD deep learning prediction model; and step 3, performing target detection by using the SSD deep learning prediction model and image information acquired by the photoelectric equipment, performing decision fusion on multiple types of information of the same target by setting a threshold range, and finally performing fusion decision on different information prediction results and multiple judgment results to determine whether the target is an unmanned aerial vehicle. Through fusion of multi-sensor information, the target detection range of the unmanned aerial vehicle is expanded, the detection efficiency is improved, and certain environmental interference can be resisted.

Description

technical field [0001] The invention relates to the fields of anti-UAV low-altitude defense and perception fusion, in particular to a UAV target detection method based on multi-sensor information fusion. Background technique [0002] In recent years, with the increasing informatization of various industries, the intelligence of aviation technology has been greatly developed. UAVs, UAV attack and defense, aerial video, forest fire prevention, environmental exploration and other fields play an important role. But at the same time, the abuse of unmanned aerial vehicles for terrorist attacks and illegal intrusions has brought many threats and hidden safety hazards, causing troubles for social security and border security. In Washington, USA, the White House encountered an illegal invasion by a quadrotor drone, which eventually crashed inside the White House wall; in France, at least 14 nuclear power plants were illegally spied on by drones. As a country with the highest depende...

AI Technical Summary

Problems solved by technology

[0007] The inventor found through research in the process of realizing the present invention that: when a single sensor detects a drone target, it often has the disadvantages of low accuracy, poor real-time performance, and weak anti-interference ability

For example, radar cannot distinguish birds, balloons and other targets when detecting drone targets; RGB cameras can obtain target appearance information to distinguish drones, but they cannot satisfy large-scale and long-distance monitoring at the same time, and it is difficult to capture suspicious targets Screen; radio frequency and audio detection equipment are extremely susceptible to interference from the surrounding environment

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