Classification and identification method for low, slow small targets

A low-slow and small target, classification and recognition technology, applied in the field of target radar detection and recognition, can solve the problems of model over-fitting and loss of implicit information, save manpower and material resources, achieve accurate recognition, and improve the ability of network classification and recognition. Effect

Pending Publication Date: 2021-03-02
LINGBAYI ELECTRONICS GRP
View PDF0 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But there will be problems in doing so. For the first method, the cut data will lead to the loss of some hidden information;...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Classification and identification method for low, slow small targets
  • Classification and identification method for low, slow small targets
  • Classification and identification method for low, slow small targets

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0017] refer to figure 1. According to the present invention, based on the surrounding geographical environment information, historical track data, collecting relevant point track data, and the PPI image formed by the original point track, different target track data are used as the training samples of the data preprocessing module; the data preprocessing module Firstly, the original data is preprocessed, and the unknown data is predicted by using the target track data law, and the track-related point track data law is automatically analyzed from the data, data prediction and data preprocessing are performed, a training set is generated, and a deep learning network is constructed. Model and network optimization module; the deep learning network model adopts deep learning algorithms and image recognition related algorithms to classify and recognize PPI images and data formed by low-slow and small targets, and classify and calculate the time series of two groups of feature-frame...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

A disclosed classification and identification method for low, slow and small targets is accurate in identification and short in identification time. The method is realized through the following technical scheme: different target track data is taken as a training sample of a data preprocessing module based on a PPI image formed by an original trace point; the data preprocessing module performs dataprediction and preprocessing on an obtained track-related plot data rule, generates a training set, constructs a deep learning network model and a network optimization module which sequentially adopta deep convolutional network DCNN and a long short-term memory network LSTM, extracts two groups of features from framing data, performs splicing to obtain joint features, and performs deep learning;image recognition target track data is input into a weighting calculation module in real time, model compression and acceleration are conducted on a trained network model structure, model compressionacceleration is achieved through weighting calculation and model pruning, depth features needed by accurate classification are achieved, and accurate recognition of classification and recognition ofsmall birds and unmanned aerial vehicles is completed.

Description

technical field [0001] The invention relates to radar detection and recognition technology for targets such as birds and unmanned aerial vehicles, and mainly relates to a classification and recognition method for "low, slow and small" targets whose flying height is below 1000 meters. Especially when the low-altitude low-power search radar detects small targets, it is a method for classifying and identifying the detected small birds and small UAVs. [0002] technical background [0003] The target characteristics and flight characteristics of birds are similar to small UAVs, and they belong to the "low, small and slow" category of targets, which will interfere with the recognition of small UAV targets. Low-slow and small targets, the full name of which is "low-altitude, slow-speed, and small-flying targets", has low observability, and the effective monitoring and identification of the two has become an urgent problem to be solved in order to ensure the safety of air routes and...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06V20/10G06N3/047G06N3/045G06F18/241G06F18/2415
Inventor 朱海鹏帅晓飞宋苏杭钱凯昌旻倩姜旭先
Owner LINGBAYI ELECTRONICS GRP
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products