Multi-feature Marine Small Target Detection Based on Multi-class Classifier

XUE Anke; MAO Kecheng; ZHANG Le

doi:10.11999/JEIT220710

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2528-2536

XUE Anke, MAO Kecheng, ZHANG Le. Multi-feature Marine Small Target Detection Based on Multi-class Classifier[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2528-2536. doi: 10.11999/JEIT220710

Citation:

XUE Anke, MAO Kecheng, ZHANG Le. Multi-feature Marine Small Target Detection Based on Multi-class Classifier[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2528-2536. doi: 10.11999/JEIT220710

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Multi-feature Marine Small Target Detection Based on Multi-class Classifier

doi: 10.11999/JEIT220710

Schlool of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Received Date: 2022-05-31
Rev Recd Date: 2022-10-15

Available Online: 2022-10-20

Publish Date: 2023-07-10

Abstract

Abstract

The pattern recognition technology have been widely used in target detection within sea clutter, and the binary pattern recognition algorithm will face the dilemma of catgory disequilibrium when dealing with this problem. The traditional method expands the target data set by adding artificial simulated target echoes, however,the detection result is easily affected by the accuracy of simulation data, and the complexity of the algorithm increases.In this paper, a multi-feature intelligent detection method for small targets within sea clutter based on multi-class classifier is proposed. Firstly, multi-dimensional features are extracted from sea clutter and target data to construct a high-dimensional feature space. Then, based on the “one to one” method of multi-class classification, the sea clutter feature space is divided into multiple subspaces, which is as large as the target data feature space to biuld multiple binary classifiers for joint decision. The binary classifier selected in this paper is the improved two-parameter K-Nearest Neighbor (K-NN) algorithm, which can effectively adjust the false alarm rate. Verified by Ice MultiParameter Imaging X-band radar (IPIX) radar data set, the detection probability of the proposed method is 82.40% when the observation time is 1.024 s, and the performance of the proposed method is improved by 2% compared with the existing feature detectors of the same type.
- Sea clutter,
- Small target detection,
- Multi-class classification,
- Two-parameter optimization K-Nearest Neighbor(K-NN) algorithm,
- Controllable false alarm

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References(29)

References

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