An Improved LPI Radar Waveform Recognition Framework With LDC-Unet and SSR-Loss

Jiang, Wangkui; Li, Yan; Liao, Mengmeng; Wang, Shafei

doi:10.1109/lsp.2021.3130797

Cited by 28 publications

(15 citation statements)

References 20 publications

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“…Over the last few years, deep learning‐based approaches have outstandingly improved the performance of image segmentation problems. U‐Net, proposed by Ronneberger et al (2015) is one of the most popular deep learning framework for semantic segmentation, widely use in the medical field and recently was also used for radar segmentation problems (Jiang et al, 2022; Nie et al, 2021). The network of U‐Net consists of contracting and expanding paths in a symmetric structure which create a u‐shaped architecture.…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

et al. 2023

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The use of weather radars to detect and distinguish between different biological patterns greatly improves our understanding of aeroecology and its consequences for our lives. Importantly, it allows us to quantify passerine bird migration at different scales. Yet, no algorithm to detect soaring bird flocks in weather radar is available, precluding our ability to study this type of migration over large spatial scales. We developed the first automatic algorithm for detecting the migration of flocks of soaring birds, an important bio‐flow phenomenon involving many millions of birds that travel across large spatial extents, with implications for risk of bird‐aircraft collisions. The algorithm was developed with a deep learning network for semantic segmentation using U‐Net architecture. We tested several models with different weather radar products and with image sequences for flock movement identification. The best model includes the radial velocity product and a sequence of two previous images. It identifies 93% of soaring bird flocks that were tagged by a human on the radar image, with a false discovery of less than 20%. Large birds such as those detected by the algorithm pose a serious risk for flight safety of civilian and military transportation and therefore the application of this algorithm can substantially reduce bird‐strikes, leading to reduced financial losses and threats to human lives. In addition, it can help overcome one of the main challenges in the study of bird migration by automatically and continuously detecting flocks of large birds over wide spatial scales without the need to equip the birds with tracking devices, unravelling the abundance, timing, spatial flyways, seasonal trends and influences of environmental conditions on the migration of bird flocks.

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Section: Methodsmentioning

confidence: 99%

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

et al. 2023

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“…To maintain the same angular resolution, the search step of the traditional WHT should be the same as that of the last epoch of the calculation of this method, as shown in Equation (15). The efficiency of this method is expressed as the ratio of the operation amount of this method to that of the traditional WHT, as shown in Equation (16).…”

Section: Parameter Optimisation Of Iterative Angle Searchmentioning

confidence: 99%

“…In refs. [13][14][15][16], a convolutional neural network (CNN), which does not require artificial features, was applied to a CWD time-frequency image. Convolutional neural network automatically extracts features and classifies various radar signals.…”

Section: Introductionmentioning

confidence: 99%

A fast Wigner Hough transform algorithm for parameter estimation of low probability of intercept radar polyphase coded signals

Yang

Chen

Dong

et al. 2023

IET Signal Processing

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It is difficult for a receiver to intercept the signals from a radar system that can emit low probability of intercept (LPI) polyphase coded signals. The traditional Wigner Hough transform (WHT) algorithm requires a large amount of computation and takes a long time to estimate the parameters of the LPI radar polyphase coded signals. To address this problem, an iterative angle search (IAS) algorithm, which when used in combination with the WHT algorithm significantly reduces the computational cost is proposed. When the signal-to-noise ratio is in the range of −4 to 20 dB, the carrier frequency, number of subcodes, and number of cycles of the carrier frequency per subcode of five polyphase coded signals, namely, Frank, P1, P2, P3, and P4, are accurately estimated in simulation experiments. Based on the selected IAS algorithm parameters, the estimation accuracy of the proposed method is the same as that of the traditional WHT algorithm. However, the operation time is only 5.14% of that of the traditional method. The IAS algorithm has certain application prospects. Experiments indicate that the proposed algorithm provides excellent performance and can rapidly and accurately estimate the parameters of LPI polyphase codes.

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“…In [3,4], instantaneous frequency and other features were extracted based on Wigner-wile Distribution (WVD) and Choi-Williams distribution (CWD) by converting radar signals to time-frequency domain, then different modulated radar signals were identified by classifying these features. Time-frequency images (TFI) were sent to a convolutional neural network after preprocessing, which can automatically learn the characteristics of different signals for recognition of modulated types [5,7]. In [8], a singleshot multi-box detector was used to identify LPI radar signals by CWD TFI, and a supplementary classifier to recognize easily confused polyphase coded signals.…”

Section: Introductionmentioning

confidence: 99%

Binarization for time-frequency images of LPI radar signals based on K-means

Yang

Chen

Dong

et al. 2023

J. Phys.: Conf. Ser.

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Low probability of intercept radar signal is widely used because it is difficult to be intercepted by non-cooperative receivers in electronic warfare. We need to binarize the time-frequency images when analyzing LPI radar signals based on time-frequency distribution. However, the existing binarization algorithms cannot distinguish noise from the signal frequency at low signal-to-noise ratios. In this paper, we propose to use K-means algorithm to binarize the gray time-frequency images of LPI radar signals. We use F1-score to comprehensively consider the effect of binarization. Based on linear frequency modulation signals, the simulation experiments show that the F1-score of the proposed algorithm exceeds the F1-score of Otsu’s method by 40% to 80% when the signal-to-noise ratio is from -6 dB to 2 dB. The binarization effect of K-means is excellent and has great application prospects.

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An Improved LPI Radar Waveform Recognition Framework With LDC-Unet and SSR-Loss

Cited by 28 publications

References 20 publications

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

A fast Wigner Hough transform algorithm for parameter estimation of low probability of intercept radar polyphase coded signals

Binarization for time-frequency images of LPI radar signals based on K-means

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