Abstract
Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.
Highlights
Radar high-resolution range profile (HRRP) is the sum of projection vectors of the echoes received in the radial radar after the signals emitted by wideband radar are scattered by targets
A mixture of random gradient Markov chain Monte Carlo (MCMC) and a cyclic variational reasoning model is proposed for scalable training and fast out-ofsample prediction. is kind of method uses a cyclic neural network to analyze and process the data of sequence structure, weakens the segmentation requirement of target attitude angle, explores the correlation within the sample, and analyses the characteristics of the model itself and the structural correlation characteristics of the internal information
To fully extract sequence correlation features [27], this study uses a bidirectional long short time network and gate structure to model the data. It proposes a dual parallel network model based on a cyclic neural network. e model extracts features from HRRP sequence data by multichannel coding and adds the output features of each classification network by dynamic weight fusion
Summary
Radar high-resolution range profile (HRRP) is the sum of projection vectors of the echoes received in the radial radar after the signals emitted by wideband radar are scattered by targets. A radar HRRP model based on the hidden Markov model is proposed in the Reference [13,14,15,16], and HMM is used to calculate the transition probability of sequence data of multirange cells In this method, the HRRP database and database index are established, and paragraphs are divided according to the azimuth angle. Is kind of method uses a cyclic neural network to analyze and process the data of sequence structure, weakens the segmentation requirement of target attitude angle, explores the correlation within the sample, and analyses the characteristics of the model itself and the structural correlation characteristics of the internal information A mixture of random gradient Markov chain Monte Carlo (MCMC) and a cyclic variational reasoning model is proposed for scalable training and fast out-ofsample prediction. is kind of method uses a cyclic neural network to analyze and process the data of sequence structure, weakens the segmentation requirement of target attitude angle, explores the correlation within the sample, and analyses the characteristics of the model itself and the structural correlation characteristics of the internal information
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