A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data

Baird, Zachary; McDonald, Michael; Rajan, Sreeraman; Lee, Simon J.

doi:10.1109/access.2020.3025144

Cited by 20 publications

(3 citation statements)

References 20 publications

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“…7 shows the ASCR under various polarization modes in Table 1. The ASCR calculation formula is as follows [34]: ASCR = 10 log 10 P T − P C P C (10) where P T represents the average power of the target bin and P C represents the average power of the sea clutter bin.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Method for Detecting Small Slow Targets on Sea Surface Based on FC-MCNN

Sun,

Zhao,

Xue

et al. 2024

IEEE Access

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Detecting small slow targets on sea surface has always been a hot topic in radar target detection. Classical target detection methods based on single input mode are particularly prone to losing the phase information of the radar echo signal, which affects the detection performance of the radar. To solve the problem of phase information loss, a False alarm Control method of Multi-input Convolutional Neural Network (FC-MCNN) is proposed in this article. First, several classic Convolutional Neural Network (CNN) models are compared and analyzed. Then, a Multi-input Convolutional Neural Network (MCNN) is designed to extract more useful information from radar echo signal. Simultaneously, in order to improve the generalization performance of the model and control the false alarm probability, a controllable false alarm Support Vector Machine (SVM) is used to modify the final output module. In addition, the datasets are also preprocessed before being input into the proposed model. Finally, the performance of the proposed method is verified by the IPIX measured data sets. The results show that the proposed method has a higher detection probability and better computational performance than the traditional CNN detection methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A Method for Detecting Small Slow Targets on Sea Surface Based on FC-MCNN

Sun,

Zhao,

Xue

et al. 2024

IEEE Access

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“…Distributed and embedded surveillance systems are wellresearched, with many contributions focusing on a variety of application scenarios, ranging from agricultural to healthcare monitoring [9], [11]- [15]. While the body of work is fairly extensive, most of the proposed designs are either not optimized for ultra-low power battery-based operation [11], [12], [15] or too inflexible for reliable surveillance, due to strict constraints on their power consumption [13], [14]. Nevertheless, some recent research has investigated low-power, battery-operated distributed surveillance systems or similar concepts.…”

Section: Related Workmentioning

confidence: 99%

WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for Distributed Surveillance

Scherer

Sidler

Rogenmoser

et al. 2022

2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling mediumand long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

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“…Neural network clutter suppression method includes convolutional neural network [ 14 , 39 , 40 , 41 ], radial basis function neural network [ 13 , 42 , 43 ], wavelet neural network [ 44 , 45 ], etc. The neural network clutter suppression method is to train and optimize itself by using the chaotic characteristics and predictability of sea clutter and, thus, to establish the prediction model of sea clutter.…”

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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A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data

Cited by 20 publications

References 20 publications

A Method for Detecting Small Slow Targets on Sea Surface Based on FC-MCNN

A Method for Detecting Small Slow Targets on Sea Surface Based on FC-MCNN

WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for Distributed Surveillance

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

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