Multi-rotor unmanned aerial vehicle target identification method based on depth normalization network

Abstract

The invention belongs to the technical field of target recognition and neural networks, and particularly relates to a multi-rotor unmanned aerial vehicle target recognition method based on a deep normalization network. Echo data of the multi-rotor unmanned aerial vehicle is preprocessed to serve as input of a depth normalization network, the depth normalization network is formed by stacking a plurality of normalization sub-networks, and then a softmax classification layer is connected in series, so that the depth normalization network is obtained. The input of each normalized sub-network is formed by splicing the output and the input of the previous normalized sub-network, so that the learned network parameters depend on the feature information of the current sub-network and the feature information of the preposed sub-network at the same time, the feature information in the original radar echo data is better described, and moreover, the learning efficiency is improved. Normalization processing is introduced to input of the sub-networks, data distribution is optimized, new distribution is more suitable for real distribution of data, and the nonlinear expression ability of the network model is further guaranteed.

Description

technical field [0001] The invention belongs to the technical field of target recognition and neural network, and in particular relates to a multi-rotor unmanned aerial vehicle target recognition method based on a deep normalized network. Background technique [0002] With the rapid development of UAV technology, UAVs have been widely used in military and civilian fields, but they have also brought security problems caused by illegal intrusion into private areas, collisions with aircraft, terrorist attacks, etc. , flight safety, etc. have brought great trouble. Therefore, it is very important to accurately identify the type of UAV. [0003] At present, the method of identifying drones mainly uses the rotating parts of multi-rotor drones to form micro-Doppler spectra with obvious differences, and then uses conventional machine learning methods for classification and recognition. However, conventional machine learning methods must be artificially set Identification features,...

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Problems solved by technology

[0003] At present, the method of identifying drones mainly uses the rotating parts of multi-rotor drones to form micro-Doppler spectra with obvious differences, and then uses conventional machine learning methods for classification and recognition. However, conventional machine learning methods must be artificially set Identification features, and for multi-rotor UAV targets, its identification features are not easy to determine manually

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