Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM

Li, Yunjie; Zhu, Mengtao; Ma, Yihao; Yang, Jian

doi:10.1049/iet-rsn.2020.0060

Cited by 50 publications

(21 citation statements)

References 37 publications

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“…In addition, neural networks have been extensively adopted to learn the PRI pattern from intercepted pulse train. Patterns stored in the neural network have achieved excellent performance in deinterleaving [16] and identifying [17]. However, the neural network methods require considerable time and workforce to label the data, and the obtained PRI pattern is not interpretable.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

Yuan

Kang

Shang

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as Pulse Repetition Interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This paper introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts, the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs, and that of encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

Yuan

Kang

Shang

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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“…MFRs can be effectively modeled as discrete event systems (DES), Visnevski first proposed a hierarchical model for g describing the behaviors of MFRs, in which the MFR signals are represented by radar pulses, radar words, and radar phrases. With the hierarchical model, several syntax-based methods [2], [5], as well as the statistical-based methods [6][7][8][9][10], have been proposed to analyze the MFR behavior state. However, most of those methods have only been proven effective for some simple radars like the Mercury [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of those methods have only been proven effective for some simple radars like the Mercury [6]. Later on, researchers focus on neural networks for MFR recognition and introduce several models such as Long Short-Term Memory networks(LSTM) [7]- [9], gated recurrent unit(GRU) [10], discrete process neural network [11]. With enough training samples, neural network-based methods outperform traditional statistical-based methods under more complex tasks [12].…”

Section: Introductionmentioning

confidence: 99%

Towards Self-supervised Learning for Multi-function Radar Behavior State Detection and Recognition

Feng¹

2022

Preprint

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<div>The analysis of intercepted multi-function radar (MFR) signals has gained considerable attention in the field of cognitive electronic reconnaissance. With the rapid development of MFR, the switch between different work modes is becoming more flexible, increasing the agility of pulse parameters. Most of the existing approaches for recognizing MFR behaviors heavily depend on prior information, which can hardly be obtained in a non-cooperative way. This study develops a novel hierarchical contrastive self-supervise-based method for segmenting and clustering MFR pulse sequences. First, a convolutional neural network (CNN) with a limited receptive field is trained in a contrastive way to distinguish between pulse descriptor words (PDW) in the original order and the samples created by random permutations to detect the boundary between each radar word and perform segmentation. Afterward, the K-means++ algorithm with cosine distances is established to cluster the segmented PDWs according to the output vectors of the CNN’s last layer for radar words extraction. This segmenting and clustering process continues to go in the extracted radar word sequence, radar phase sequence, and so on, finishing the automatic extraction of MFR behavior states in the MFR hierarchical model. Simulation results show that without using any labeled data, the proposed method can effectively mine distinguishable patterns in the sequentially arriving PDWs and recognize the MFR behavior states under corrupted, overlapped pulse parameters.</div>

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“…Early techniques used in the traditional interceptors like the template matching [15,16] or the statistical histogram methods [17] are incapable of dealing with the dynamically varying behaviours in the CR. Later studies investigated the automatic modulation recognition (AMR) of both inter-and intra-pulse modulations (or modulation on pulse; MOP) [18][19][20][21][22][23][24][25][26][27][28][29][30] through recognising the modulation patterns of different parameters in a pulse sequence. These methods were designed initially to recognise the modulation type of a single control parameter only.…”

Section: Introductionmentioning

confidence: 99%

JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network

Zhu

Zhang

et al. 2021

IET Radar Sonar & Navi

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The newly developed cognitive radar (CR) can implement flexible work modes defined with a set of mode definition parameters. Each definition parameter can employ its modulation type and corresponding optimised modulating values. Automatic recognition and analysis of CR work mode are significant challenges for electromagnetic reconnaissance applications. In this article, a deep multitask neural network is proposed for Joint automatic Modulation Recognition and modulation Parameter Estimation (JMRPE-Net) for the emerging task of CR signals analysis. The proposed JMRPE-Net consists of a fork-shaped architecture in which three cascaded convolutional layers act as a shared module for the extraction of common features followed by multiple branches of long short-term memory layers with the attention mechanism for task-specific features extraction. The proposed network can receive a sequence of CR pulse signals as input and parallelly perform automatic modulation recognition (AMR) and modulation parameter estimation tasks for multiple work mode definition parameters. Extensive experiments are conducted based on simulated radar intermediate frequency signals with consideration of imperfections of real-world electromagnetic environments. The experimental results validate the superiority of the proposed JMRPE-Net against the existing state-of-the-art single task AMR methods or parameter estimation methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM

Cited by 50 publications

References 37 publications

Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

Towards Self-supervised Learning for Multi-function Radar Behavior State Detection and Recognition

JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network

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