Discriminant Analysis for Radar Signal Classification

Guo, Shanzeng; Tracey, Hannah

doi:10.1109/taes.2020.2965787

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…The three features of envelope rising edge time [16] (rising edge slope), pulse width, and envelope top drop of different radar individuals are the least affected by environmental factors and the most reliable, and they do not fluctuate significantly with changes in the working mode and environmental factors of radar individuals. At the same time, they are less disturbed by multipath effects and other forms of interference in the transmission process, so these three features are commonly selected as fingerprint features on the envelope of radar radiation source individuals for analysis [27]. Figure 1 illustrates three different envelope fingerprint features.…”

Section: Signal Envelope Fingerprint Featuresmentioning

confidence: 99%

“…In order to simulate the nonlinear variations in transmitted signals caused by physical hardware as accurately as possible, we referenced the characteristics of signals received by radar receivers in reality and several validated research articles from various dimensions, such as the time domain and frequency domain [14,[26][27][28][29][30][31]. In our simulation, we first modeled three different radar systems by adjusting parameters such as pulse width (PW), bandwidth (BW), and radio frequency (RF), also known as the carrier frequency.…”

Section: Dataset and Parameter Settingmentioning

confidence: 99%

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Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

Jing,

Li,

et al. 2024

Remote Sensing

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To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

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Section: Signal Envelope Fingerprint Featuresmentioning

confidence: 99%

Section: Dataset and Parameter Settingmentioning

confidence: 99%

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

Jing,

Li,

et al. 2024

Remote Sensing

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“…We choose nine common classifiers to recognize emitter identification, containing kernel support vector machine (KSVM) [20], probabilistic neural Fig. 7 Plots of moments for various emitters network (PNN) [12], k-nearest neighbor (KNN) [14], discriminant analysis classifier (DAC) [21], and their variants. Results are presented in Fig.…”

Section: Classificationmentioning

confidence: 99%

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Zhao

Wang

Sun

et al. 2023

EURASIP J. Adv. Signal Process.

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Extensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalization ability, we analytically investigate how intentional modulation and unintentional modulation affect moments. A framework for extracting moment-based fingerprint features is proposed through fine-segmenting slices. Fingerprint features are extracted, followed by segmenting signals into a combination of sinewaves and calculating their moments. The proposed framework shows advantages in mechanism interpretability and generalizing ability. Simulations and experiments verified the correctness and effectiveness of the proposed framework.

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“…The DA classifier could be linear DA (LDA) or quadratic DA (QDA). LDA and QDA are widely used primarily due to their inherent capacity to address many multiclass problems without the requirement to tune hyperparameters to improve their classification accuracy [56]- [58].…”

Section: A Discriminant Analysis (Da) Classifiermentioning

confidence: 99%

Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning

Sangodoyin¹,

Akinsolu

Pillai

et al. 2021

IEEE Access

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Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), k-nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical midsized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed (5.3 ×10 5 observations per second), training time (12.4 ms), and robustness.

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Discriminant Analysis for Radar Signal Classification

Cited by 10 publications

References 28 publications

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

A novel framework for extracting moment-based fingerprint features in specific emitter identification

Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning

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