Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets

DING Hao; LI Ao; CAO Zheng; LIU Ningbo; WANG Guoqing; SUN Dianxing

doi:10.11999/JEIT251119

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DING Hao, LI Ao, CAO Zheng, LIU Ningbo, WANG Guoqing, SUN Dianxing. Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251119

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DING Hao, LI Ao, CAO Zheng, LIU Ningbo, WANG Guoqing, SUN Dianxing. Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251119

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Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets

doi: 10.11999/JEIT251119 cstr: 32379.14.JEIT251119

1.
Naval Aviation University, Yantai 264001, China
2.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62388102, 62101583)

Received Date: 2025-10-23
Accepted Date: 2026-02-09
Rev Recd Date: 2026-02-09

Available Online: 2026-03-01

Abstract

Abstract

Objective In radar maritime target detection scenarios, when two or more targets are located within the same range cell, mixture echoes are generated, such as echoes containing both ships and floating targets. Existing target recognition methods exhibit notable limitations in these scenarios because they typically focus on the Doppler channel with the strongest energy in the time-frequency domain. To address this issue, a target recognition method that integrates mode reconstruction and time-frequency features is proposed. The aim is to distinguish individual targets without prior knowledge of whether the received echoes contain mixture targets, thereby avoiding reliance on high range resolution or multipolarization information. Methods The core idea is to introduce Variational Mode Decomposition (VMD) to decompose radar echoes into multiple modal components, thereby enabling Doppler-channel separation. To address spurious modes and the fragmented representation of a single target across multiple modes after decomposition, an energy-constrained mode filtering method and a spectral-consistency-based mode clustering method are proposed for effective mode selection and reconstruction. Based on the reconstructed signals, time-frequency differences between ships and floating targets are analyzed in terms of micromotion and signal complexity. Features are extracted from two perspectives: motion stability and the disorder degree of energy distribution, referred to as VF and REDDC features, respectively. These features enable accurate identification of individual targets. Results and Discussions Experiments are conducted using X-band radar measured data under sea states 2–4 (Table 1 and Table 2). The results show that the proposed method achieves an average recognition accuracy of 97.32% in mixture scenarios. This performance significantly exceeds that of the existing four-feature recognition method (Table 3) and other advanced methods (Fig. 9). The effect of frequency separation between different targets is further examined. When the time-frequency ridge spacing exceeds 70 Hz, the recognition accuracy reaches 97.93% (Fig. 11). This result also provides empirical guidance for selecting an appropriate clustering threshold during the mode reconstruction stage. When mixture scenarios change to single-target scenarios due to relative motion, the proposed method achieves an average recognition accuracy of 93.34%. This value is 4.62% higher than that of the existing four-feature method (88.72%) (Table 4). Additional analysis indicates that the observation duration used for feature extraction should be no less than 0.25 s to maintain the expected recognition accuracy (Fig. 12). Conclusions This study examines recognition problems in maritime multi-target mixture scenarios. VMD is applied to separate the constituent components of mixture echoes. To address spurious modes and fragmented representation of target information across multiple modes, an energy-constrained mode filtering method and a spectral-consistency-based mode clustering method are proposed. VF and REDDC features are extracted from the perspectives of structural characteristics and signal complexity. A Support Vector Machine (SVM) classifier is then used for target recognition. Performance analysis confirms that the proposed method effectively identifies each constituent target in mixture echoes and maintains strong recognition performance in single-target scenarios. Future work will improve computational efficiency and real-time capability by optimizing the stopping criteria of VMD iterations and will further examine the application boundaries of the method using measured data under higher sea states.
- Maritime target recognition,
- Mixture scenarios,
- Variational Mode Decomposition (VMD),
- Time-frequency analysis

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References(29)

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