To address the issue of low accuracy in intelligent trajectory association for radar data,this paper proposes a trajectory association method that integrates multi-scale convolution(CNN)and improved long short-term memory networks(LSTM).Firstly,multi-scale CNN is utilized to extract spatial features across multiple dimensions,avoiding the limited field of view problem caused by fixed-size convolution kernels and enabling the network to capture higher-dimensional features.Subsequently,the features are fed into the improved LSTM network to capture temporal features,and the enhanced unit structure takes into account the correlation of adjacent moments in trajectory data,effectively suppressing the impact of noise and errors.Finally,the results from simulation experiments demonstrate that this method effectively improves trajectory association accuracy.
关键词
卷积神经网络/长短期记忆神经网络/航迹关联
Key words
convolutional neural network/long short-term memory neural network/track association
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