Robust Maritime Target Detector in Short Dwell Time

Lee, Myung-Jun; Kim, Jieun; Ryu, Bo-Hyun; Kim, Kyung‐Tae

doi:10.3390/rs13071319

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…Another index, the detection rate PD of the target detector, is given in the form of the following [ 75 ]:

where

is the total number of target points and

is the total number of detected target points.…”

Section: Resultsmentioning

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%.

show abstract

“…Another index, the detection rate PD of the target detector, is given in the form of the following [ 75 ]:

where

is the total number of target points and

is the total number of detected target points.…”

Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…To evaluate the robustness and effectiveness of the proposed method, several widely used methods based on sea clutter correlation analysis are taken for performance comparison. These methods include CF (the SACT in Section 2.1.1), CSM [22,23], and CANMF [18,19]. CF is the description of the auto-correlation of sea clutter, which uses the auto-correlation time to distinguish the sea clutter.…”

Section: Comparison With the Existing Algorithmsmentioning

confidence: 99%

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Luo

Zhang

et al. 2022

Remote Sensing

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The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms.

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“…(1) For each input picture, each layer of the neural network performs multi-core convolution on it, and each convolution kernel corresponds to a feature map generated after convolution, so multiple convolution kernels are convolved at the same time to form a feature map cube. (2) The sliding window has no fixed size; it is set according to the size of the image in the dataset. The structure of the two-stage detection Faster R-CNN network is shown in Figure 3.…”

Section: Radar Ppi Image Target Detection Based On the Two-stage Detection Algorithm Faster R-cnnmentioning

confidence: 99%

“…Radar is one of the main sensors for marine target detection, which can detect targets by emitting electromagnetic waves without being restricted by day and night, and can also penetrate clouds, rain, and fog [1]. In fact, the sea environment is complex and the marine targets are diverse, so the target's returns are sometimes weak, which makes radar detection rather difficult [2,3]. Adaptive and robust detection of marine targets is the key technology, which has received widespread attention from researchers all over the world [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Chen

Guan

et al. 2021

Remote Sensing

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Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

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Robust Maritime Target Detector in Short Dwell Time

Cited by 6 publications

References 31 publications

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

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

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

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