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As low-resolution radar is still the main radar in service in China, the ground target classification and recognition technology of low-resolution radar has a wide application prospect in modern military and civil fields. This paper mainly studies and compares two main types of automatic target recognition and classification method for low-resolution ground radar: conventional recognition based on feature extraction and neural networks, and the conclusion is that the latter has better performance and needs less time to train. The former model in this paper fuses the time domain and frequency domain features of ground target echo, then simulates, compares and analyzes the performance of different classifiers. The classifiers studied include: naive bayes classifier (NBC), decision tree classifier (DT), linear discriminant analysis (LDA) classifier, k nearest neighbors (KNN) classifier and support vector machine (SVM) classifier. Five-fold cross validation is adopted in the experiment to effectively avoid the impact of arbitrariness on the results caused by the random division of the sample set into training sample set and test sample set. Besides, based on conventional convolutional neural networks, a new neural network structure named multi-scale residual neural network (Multi-scale ResNet) is proposed for one-dimensional feature target recognition, which effectively reduces the data dimension through auto-encoder and solves the problem of performance degradation caused by the difficulty in training too many levels of traditional convolutional neural network. The bayesian hyper-parameter optimization method is utilized to optimize the hyper-parameters of different classifiersl. Finally, compared the accuracy of the two types of target recognition, the best performance of the pattern recognition is the support vector machine, which recognition rate is 91.2%, while multi-scale residual neural network recognition rate is up to 99.6%. |
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Neural networks
Radar
Target recognition
Convolution
Automatic target recognition
Feature extraction
Convolutional neural networks
