Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar

Yang, Zhuo; Tang, Jianjiang; Zhou, Hao; Xu, Xinjun; Tian, Yingwei; Wen, Biyang

doi:10.3390/rs13081548

Cited by 14 publications

(13 citation statements)

References 41 publications

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“…[32]. However, later works verified that more general distributions are more appropriate for the overall clutter, such as Weibull [15, 33] and K distributions [14], considering the complex scattering mechanisms described previously.…”

Section: High‐frequency Surface Wave Radar Modelmentioning

confidence: 99%

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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: High‐frequency Surface Wave Radar Modelmentioning

confidence: 99%

“…In ref. [15], time‐frequency analysis is used to identify target ridges, with a Weibull cell‐averaging CFAR applied to the detected ridge. While the detector was found to be capable of identifying targets within first‐order peaks, it had trouble distinguishing targets in close frequencies.…”

Section: Introductionmentioning

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

“…In the same manner, this method only has perfect effects on linear frequency modulation signals and needs the radar to accumulate pulse for a long time in dwell mode. In addition, there is another time-frequency analysis that maps one-dimensional time signal to two-dimensional TF plane by using TF joint representation in [ 56 , 57 , 58 , 59 ]. Similarly to the time-domain cancellation method, this method also needs to use the Doppler information of the radar signal.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

Entropy

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In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.

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“…Although their method detected weak ship signals successfully, its disadvantages remain the same as [68]. For HFSWR, Yang et al [70] proposed a TF domain binary integration method to improve the detection of weak target signals, and they reported that the combination of CFAR and TF-CFAR can lead to a further improvement. Although the time-frequency binary integration CFAR (TF-BI-CFAR) in [70] improves the performance of weak targets' detection, the method still suffers from the masking problem due to strong clutter and large targets, so that the detection performance in the scenarios of multi-target and clutter edge is poor.…”

Section: Introductionmentioning

confidence: 99%

“…For HFSWR, Yang et al [70] proposed a TF domain binary integration method to improve the detection of weak target signals, and they reported that the combination of CFAR and TF-CFAR can lead to a further improvement. Although the time-frequency binary integration CFAR (TF-BI-CFAR) in [70] improves the performance of weak targets' detection, the method still suffers from the masking problem due to strong clutter and large targets, so that the detection performance in the scenarios of multi-target and clutter edge is poor. Later, Yang et al [71] found that the log-normal distribution was optimal to model sea clutter in the TF domain, and with this model they achieved a better performance for weak and non-stationary target detection than other conventional CFAR detectors.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

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As one of the main sensors for continuous maritime measurements of sea state parameters, high-frequency surface wave radar (HFSWR) also plays an important role in ship detection and tracking. Compact HFSWR often suffers from missing targets, especially when the target appears near the Doppler region with heavy sea clutter or near another target in a multi-target scenario. To address this problem, an automatic ship detection method based on time–frequency (TF) analysis is presented in this paper. The TF target ridge areas are extracted in the TF image via the eigenvalues of the Hessian matrix, image edge detection, and local maximum search. Then, whether ship signals exist in the TF ridges or not is decided by a decision threshold that is calculated by fitting the probability distribution function (PDF) of sea clutter in the TF domain. The proposed TF method can separate TF ridges of similar Doppler frequency and performs constant false alarm rate (CFAR) detection for TF targets, which facilitates detecting these targets that are masked by sea clutter and other large targets. Experimental results show that the number of detected ships that match with the automatic identification system (AIS) records is four times more than that obtained by the conventional constant false alarm rate (CFAR) detectors and 1.3 times more than that by the state-of-the-art TF method in consideration of approximately the same number of detected targets.

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Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar

Cited by 14 publications

References 41 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

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

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

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