Software for marine ecological environment comprehensive monitoring system based on MCGS

Wang, X. H.; Ma, Ronghua; Cao, Xuebo; Cao, Lu; Chu, Dongzhi; Zhang, L.; Zhang, Tianyuan

doi:10.1088/1755-1315/82/1/012087

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Radar observation is a key approach for dynamic monitoring of marine environment. The observation of ocean surface streams with various radars, namely, high-frequency radar, X-band radar, and synthetic aperture radar, plays an important role in marine rescue, oil discharge, navigation and transport, military sailing, and fishery [1][2][3][4][5][6][7][8][9]. Distributed radars with high spatiotemporal resolution, low cost, and long detection range are applied extensively in marine environment monitoring [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Liu¹,

Antypenko²,

Sushko³

et al. 2021

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Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

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