Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Liu, Zhang‐Meng

doi:10.1109/taes.2021.3079571

Cited by 18 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the extensive use of radar signals in recent years, various types of interference and signal parameter spatial overlap phenomena have increased, creating a complex electromagnetic environment around the radar signal [1]. Signals in a complex environment are specifically characterized by certain measurement errors, more lost pulses, and false pulses, and these characteristics greatly interfere with the signals [2].…”

Section: Introductionmentioning

confidence: 99%

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Tang,

Wei

2024

Electronics

View full text Add to dashboard Cite

Radiation source signal sorting in complex environments is currently a hot issue in the field of electronic countermeasures. The pulse repetition interval (PRI) can provide stable and obvious parametric features in radiation source identification, which is an important parameter relying on the signal sorting problem. To solve the problem linked to the difficulties in sorting the PRI in complex environments using the traditional method, a signal sorting method based on a parallel denoising autoencoder is proposed. This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully connected layers to achieve the simultaneous sorting of multiple signal types in the overlapping signals. The experimental results show that this method maintains high precision in scenarios prone to large error and can efficiently filter out noise and highlight the original features of the signal. In addition, the present model maintains its performance and some robustness in the sorting of different signal types. Compared with the traditional algorithm, this method improves the precision of sorting. The algorithm presented in this study still maintains above 90% precision when the pulse loss rate reaches 50%.

show abstract

Section: Introductionmentioning

confidence: 99%

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Tang,

Wei

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…There are three main categories of deinterleaving methods: PRI‐based methods [10–18], machine learning (ML) methods based on clustering [19–33], and deep learning (DL)‐based methods [8, 34–42].…”

Section: Introductionmentioning

confidence: 99%

“…There are three main categories of deinterleaving methods: PRI-based methods [10][11][12][13][14][15][16][17][18], machine learning (ML) methods based on clustering [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], and deep learning (DL)-based methods [8,[34][35][36][37][38][39][40][41][42].…”

mentioning

confidence: 99%

An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

Al‐Malahi

Farhan

Feng

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The accuracy of radar emitter signal sorting nowadays deteriorates due to the high flexibility and complexity of modern radar pulse streams and the density of crowded electromagnetic environment. In modern radar signal sorting based on pulse repetition interval, conventional methods usually fail to achieve acceptable accuracy and lack stable performance for two main reasons: (1) Conventional methods require a large number of pulses in the stream, which is not practical in many applications. (2) These methods are sensitive to pulse loss and random noise pulses. These two reasons are the main problem that is addressed in this paper. Our proposed model is a machine learning architecture called Unified Residual Recurrent Neural Network (URRNN). In this architecture, residual neural network and recurrent neural network are combined and modified to alleviate the forementioned shortcomings of traditional approaches and enhance the model performance in both classification and deinterleaving tasks. This aim is achieved due to the fact that URRNN extracts both spatial and temporal features, which means more information about processed stream that is exploited to enhance model performance. Three different architectural combinations of URRNN, which show high accuracy and reasonable processing time, are built and trained. The structural and functional description are provided for each architecture. Simulation demonstrates high accuracy and reliable performance of the proposed methods in different circumstances. The results are compared with the results obtained by other conventional machine learning techniques.

show abstract

“…In addition to the typical de-interleaving algorithms described above, other types of signal de-interleaving algorithms have also been proposed, including signal de-interleaving algorithms based on square sine wave interpolation [21,22], signal de-interleaving algorithms based on sequence correlation [23][24][25], and signal de-interleaving algorithms based on machine learning [26][27][28][29]. In 2007, Jiang et al proposed a square sine wave interpolation algorithm [21], estimating PRI with high accuracy, speed, and anti-pulse loss.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for De-Interleaving Staggered PRI Signals

et al. 2023

View full text Add to dashboard Cite

Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal form of SAR. It is important for anti-SAR reconnaissance to de-interleave the staggered PRI signal from the mixed signals. To address the problem that the existing staggered signal de-interleaving algorithms cannot accommodate PRI jitter and are computationally inefficient, this paper proposes an efficient algorithm for de-interleaving staggered PRI signals. A clustering-based square sine wave interpolation method and a threshold criterion are proposed, improving computational efficiency while suppressing interference between sub-PRIs and the frame period of the staggered PRI signal. In addition, a sequence retrieval algorithm incorporating matched filter theory is proposed to improve the separation accuracy of radar pulse sequences. The simulation shows that the novel algorithm can adapt to PRI jitter and de-interleave staggered PRI signals from mixed signals with high efficiency. Compared with the existing staggered signal de-interleaving algorithm, the computational efficiency is improved by an order of magnitude.

show abstract

Pulse Deinterleaving for Multifunction Radars With Hierarchical Deep Neural Networks

Cited by 18 publications

References 26 publications

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder

An intelligent radar signal classification and deinterleaving method with unified residual recurrent neural network

An Efficient Algorithm for De-Interleaving Staggered PRI Signals

Contact Info

Product

Resources

About