A Novel iTransformer-Based Approach for AIS Data-Assisted CFAR Detection

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Abstract
Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection.
Funding Information
  • National Natural Science Foundation of China (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)
  • Key Projects of National Key R & D Program (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)
  • Natural Science Project of Fujian Province (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)
  • Natural Science Founda-tion of Xiamen, China (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)
  • Chunhui Project Foundation of the Education Department of China (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)
  • University Natural Science Foundation (52371369, 2021YFB390150, 2022J01323, 2021J01822, 2020J01660, 20230019, 3502Z202473059, 3502Z202573055, 3502Z202471079, 202200324, ZQ2024110)

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