Multi-target classification method based on vehicle-mounted millimeter-wave radar combined with svm and cnn

A millimeter wave radar and classification method technology, applied in the field of joint support vector machine and convolutional neural network target classification, can solve the problems of ignoring position information and poor classification effect, achieve strong generalization ability, avoid artificial feature extraction, improve The effect of accuracy

Active Publication Date: 2021-10-19
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Convolutional neural networks are sensitive to training set sample imbalance, and the "more imbalanced" the category, the worse the classification effect
At the same time, because the convolution kernel is used to extract the local features of the image in the convolutional neural network, the position information of the target itself is ignored during the classifier training process, and these features can also be used for target classification in some cases.

Method used

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  • Multi-target classification method based on vehicle-mounted millimeter-wave radar combined with svm and cnn
  • Multi-target classification method based on vehicle-mounted millimeter-wave radar combined with svm and cnn
  • Multi-target classification method based on vehicle-mounted millimeter-wave radar combined with svm and cnn

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] Such as figure 1 As shown, based on the vehicle-mounted millimeter-wave radar joint SVM and CNN multi-target classification method, including: training phase and testing phase; the training phase includes the following steps:

[0055] Step 1. Obtain the intermediate frequency signal f(t) after the target echo signal is processed by the millimeter wave radar system, and calibrate the classification label for each obtained intermediate frequency signal f(t);

[0056] The millimeter-wave radar system is installed on the vehicle, and the millimeter-wave radar system includes a radar transmitter, a radar receiver and a mixer; the radar transmitter periodically transmits a chirp signal, and the radar receiver receives the echo scattered by the target. Wave signal, the mixer uses the chirp signal transmitted by the radar to mix the received echo signal to obtain the intermediate frequency signal f(t);

[0057] In the training phase, pedestrians, bicycles and cars are respecti...

Embodiment 2

[0095] Embodiment 1 constructs a sample set by acquiring the intermediate frequency signal f(t) processed by the millimeter wave radar system on the target echo signal. When the number of collected intermediate frequency signals f(t) of known target categories is insufficient, the intermediate frequency signal f(t) can be generated by means of simulation. Using signal simulation to generate intermediate frequency signals corresponding to echo signals of pedestrians, bicycles, and cars, you can refer to the Chinese invention patent application with application number 2019104895140.

[0096] In this embodiment, 40 IF signals of pedestrians, 80 IF signals of bicycles and 200 IF signals of automobiles are generated through signal simulation. Process each generated IF signal to generate multiple range-Doppler maps, and extract 5 range-Doppler maps at equal intervals from the multiple range-Doppler maps generated by each IF signal, totaling 1600, And calibrate the category label of...

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Abstract

The invention discloses a multi-target classification method based on vehicle-mounted millimeter-wave radar combined with SVM and CNN. First, the IF signals of different targets acquired by the radar are sampled, converted into frame signals, and two-dimensional Fourier transform is performed on the frame signals. After normalization, the distance-Doppler map is obtained, the feature vector in the distance-Doppler map is extracted, and the sample set is constructed; secondly, the SVM classifier is constructed and trained, and the SVM classifier capable of preliminary classification is obtained; finally, it is constructed and trained The convolutional neural network classifier further classifies the samples that the SVM classifier cannot classify, and obtains the classification result of the target to be tested. This method combines the SVM classifier and the CNN classifier to make up for the insensitivity of the CNN classifier to the position information, make full use of the effective information of the samples, and improve the accuracy of the target classification.

Description

technical field [0001] The invention relates to a target classification method based on a vehicle-mounted millimeter-wave sensor, in particular to a target classification method based on a vehicle-mounted millimeter-wave sensor combined support vector machine and convolutional neural network. Background technique [0002] In recent years, with the continuous improvement of the market's demand for active safety and intelligence of automobiles, the huge social and economic value of unmanned driving has become more and more prominent. More and more enterprises and scientific research institutions have actively participated in and promoted the development of unmanned driving. Since the automotive industry has extremely high requirements for pedestrian safety, pedestrian and vehicle classification has gradually become a key technology in driverless driving. In the field of autonomous driving, unmanned vehicles must have the ability to identify pedestrians and vehicles and their l...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/12
Inventor 武其松高腾
Owner SOUTHEAST UNIV
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