Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

Yang, Zhuo; Zhou, Hao; Tian, Yingwei; Huang, Weimin; Shen, Wei

doi:10.3390/rs13214305

Cited by 8 publications

(2 citation statements)

References 79 publications

(95 reference statements)

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“…In ref. [9], a ship detection method based on time-frequency analysis is proposed. Hessian matrix segmentation and image edge detection are used to identify ridges containing targets in the time-frequency domain.…”

Section: Related Workmentioning

confidence: 99%

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

Costa

Medeiros

Saotome

et al. 2023

IET Radar Sonar & Navi

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This article presents a deep neural network‐based constant false alarm rate (NNB‐CFAR) detector for simulated high‐frequency surface wave radar (HFSWR) data. A deep neural network is trained to identify fluctuation parameters of each cell of a range‐Doppler power spectrum based on the patterns present in the neighbouring cells. The estimated parameters are then used for calculating a detection threshold with a user‐specified probability of false alarm. To train the network, a realistic model of HFSWR echoes is used for generating a large labelled range‐Doppler image dataset, including many possible clutter scenarios and interfering target echoes. Several CFAR windows are extracted from the training range‐Doppler dataset and used as training data. The neural network is trained to replicate the output of a maximum likelihood estimator based on the reference cells of the CFAR window. The NNB‐CFAR algorithm was then compared to traditional CFAR algorithms by identifying targets in the second set of simulated range‐Doppler images. The probability of detection was also experimentally measured in the context of HFSWR for all algorithms. Results show that the technique can significantly improve detection rates amid strong clutter.

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Section: Related Workmentioning

confidence: 99%

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

Costa

Medeiros

Saotome

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…High-frequency surface wave radar (HFSWR) is widely utilized in maritime applications for over-the-horizon target detection [10][11][12] and ocean remote sensing [13][14][15][16] due to its unique capabilities. Furthermore, there have been significant advances in HF-SWR ionospheric-sounding research in recent years [17,18].…”

Section: Introductionmentioning

confidence: 99%

Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa

Wang,

Lyu,

et al. 2024

Remote Sensing

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Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions.

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An improved CFAR algorithm for multiple environmental conditions

Rihan,

Nossair,

Mubarak

2024

SIViP

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The primary objective of radar digital signal processing is to detect and identify targets in complicated situations, such as those involving clutter or several closely positioned targets. The constant false alarm rate (CFAR) method is more effective for target recognition and has better control over the false alarm rate. Many studies have been conducted on the design of CFAR, but previous CFAR algorithms have not been effective in all or most environmental fields and target scenarios. In this study, an algorithm called Censored Mean Clutter Map CFAR (CM-CM CFAR) has been developed and tested for various environmental conditions. When compared to a fixed false alarm rate, the suggested CFAR algorithm’s Monte Carlo simulation results demonstrated a high detection probability in a variety of environments. This work designs a real-time CM-CM CFAR processor using field-programmable gate array (FPGA) technologies. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. Consequently, the implementation required 23,741 LUTs and 1825 FF. It is verified that the complexity and operating speed of the suggested CFAR processor are extremely appropriate for real-time implementation when compared to the results of the previously proposed FPGA implementation.

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Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

Cited by 8 publications

References 79 publications

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

A CFAR‐like detector based on neural network for simulated high‐frequency surface wave radar data

Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa

An improved CFAR algorithm for multiple environmental conditions

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