Sequence-Feature Detection of Small Targets in Sea Clutter Based on Bi-LSTM

Wan, Hao; Tian, Xiaoqing; Liang, Jing; Shen, Xiaofeng

doi:10.1109/tgrs.2022.3198124

Cited by 18 publications

(7 citation statements)

References 32 publications

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“…This section of the experiment uses the IPIX dataset shown in Table 3 to compare the detection performance of the proposed IMIRV2 detector against three other detectors: the tri-feature detector [4], the support vector machine (SVM) detector [27], and the Bi-LSTM detector [28].…”

Section: Detection Performance Comparisonmentioning

confidence: 99%

Sea-Surface Small Target Detection Based on Improved Markov Transition Fields

Ye,

Xing,

Zhou

2024

JMSE

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Addressing the limitations of manually extracting features from small maritime target signals, this paper explores Markov transition fields and convolutional neural networks, proposing a detection method for small targets based on an improved Markov transition field. Initially, the raw data undergo a Fourier transform, feature fusion is performed on the series, and a spectrogram is generated using Markov transition fields to extract radar data features from both the time domain and frequency domain, providing a more comprehensive data representation for the detector. Then, the InceptionResnetV2 network is employed as a classifier, setting decision thresholds based on the softmax layer’s output, thus achieving controllable false alarms in the detection of small maritime targets. Additionally, transfer learning is introduced to address the issue of sample imbalance. The IPIX dataset is used for experimental verification. The experimental results show that the proposed detection method can deeply mine the differences between targets and the maritime clutter background, demonstrating superior detection performance. When the observation time is set to 1.024 s, the IMIRV2 detector performs best. Cross-validation with different data preprocessing methods and classification models reveals a significant advantage in the performance of the IMIRV2 detector, especially at low signal-to-noise ratios. Finally, a comparison with the performance of existing detectors indicates that the proposed method offers certain improvements.

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Section: Detection Performance Comparisonmentioning

confidence: 99%

Sea-Surface Small Target Detection Based on Improved Markov Transition Fields

Ye,

Xing,

Zhou

2024

JMSE

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“…The LSTM algorithm can learn longterm dependencies, primarily to address the issues of gradient vanishing and exploding gradients while training long sequences. The LSTM deep neural network consists of three types of gates [28]: the forget gate, the input gate, and the output gate. These three gates control the memory state of previous, input, and output information, respectively, ensuring that the network can better learn long-distance dependencies.…”

Section: Modeling Processmentioning

confidence: 99%

A Short-Term Forecasting Method for High-Frequency Broadcast MUF Based on LSTM

Ji,

He,

et al. 2024

Atmosphere

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This paper proposes a short-term forecasting method for high-frequency broadcast Maximum Usable Frequency (MUF) based on Long Short-Term Memory (LSTM) to meet the demand for refined and precise high-frequency broadcast coverage. Based on the existing infrastructure of broadcast and television stations, we established an experimental verification system to collect data for approximately three years. Two links were selected based on data quality: Urumqi to Lhasa and Lanzhou to Lhasa. A short-term forecast of MUF was conducted using the data from these two links. Comparison and analysis were conducted between the forecasting results of our model and those of the REC533 model. Our proposed method outperforms the REC533 forecasting results overall, with a reduction in root mean square error (RMSE) of 0.66 MHz and an improvement in forecast accuracy of 14.77%. The comparison result demonstrates the feasibility and accuracy of our model.

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“…The enhanced CNN structure 27 takes full advantage of the latent information in time-frequency inputs. Wan et al 28 proposed a method for detecting small maritime targets based on sequence feature extraction. In their method, the instantaneous phase, Doppler spectrum entropy, and short-time Fourier transform marginal spectrum are combined to train a Bi-LSTM model to conduct target detection.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The above traditional methods are difficult to achieve satisfactory detection performance in practical applications. Hence, researchers [21][22][23][24][25][26][27][28] began to explore the method based on deep learning. For example, Chen et al 21 took the Doppler spectrum and radar echo amplitude as the original input, and used a dual-channel convolutional neural network and a false alarm controllable classifier to extract features and obtain prediction results.…”

Section: Background and Related Workmentioning

confidence: 99%

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

Wang¹,

Li²

2023

Sci Rep

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Radar detection of maritime targets plays an important role in marine environment monitoring. For civil maritime detection in the areas of inshore coastal, pulse-compression radar is universally used owing to its low cost. The complex sea clutter in the practical application will greatly affect the received radar echoes. Due to the inability to accurately describe the differences in characteristics between sea clutter and maritime targets, the detection performance of methods based on mathematical derivation is not satisfactory in actual deployment. Recently, neural-based methods have made strides in many pattern recognition tasks, such as computer vision and natural language processing. The sophisticated deep neural models can be applied to different downstream tasks due to their powerful learning ability. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on deep learning. To better model the sequence correlation of radar echoes, we propose a Self-Adaption Local Augmented Long Short-Term Memory (SALA-LSTM) structure. The proposed SALA-LSTM integrates adaptive convolution into vanilla LSTM cells, which not only maintains the inherent overall sequence modeling ability of vanilla LSTM, but also strengthens its ability to perceive the correlation on a small scale in the local scope. Based on SALA-LSTM and other neural structures, we propose a radar target detection network. A measured dataset containing different typical scenarios is utilized to evaluate the detection probability and false alarm rate. The detection performance of our proposed network is superior to that of the existing methods.

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Sequence-Feature Detection of Small Targets in Sea Clutter Based on Bi-LSTM

Cited by 18 publications

References 32 publications

Sea-Surface Small Target Detection Based on Improved Markov Transition Fields

Sea-Surface Small Target Detection Based on Improved Markov Transition Fields

A Short-Term Forecasting Method for High-Frequency Broadcast MUF Based on LSTM

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning

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