Approximating the Neyman–Pearson detector with 2C-SVMs. Application to radar detection

Mata-Moya, David; Jarabo-Amores, María-Pilar; Nicolás, Jaime Martín de; Rosa-Zurera, Manuel

doi:10.1016/j.sigpro.2016.08.021

Cited by 14 publications

(9 citation statements)

References 28 publications

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“…[22] proposed multigene genetic programming-(MGGP-) based feature engineering was conducted to transform the cumulants of the received signals into highly discriminative features, and then, authors use a logistic regression classifier to achieve classification of overlapping signal modulation. [23] presented a study about the possibility of implementing approximations to the Neyman-Pearson (NP) detector with C-Support Vector Machines (C-SVM) and 2C-SVM. It was based on obtaining the functions that these learning machines approximate to after training to minimize the empirical risk, and on the possible implementation of the NP detector with these approximated functions.…”

Section: Related Workmentioning

confidence: 99%

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A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Pan

Lin

2020

International Journal of Digital Multimedia Broadcasting

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In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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Section: Related Workmentioning

confidence: 99%

“…uses logistic regression to distinguish signals, and [23] uses SVM to achieve radar signal recognition. Our method is compared with these two machine learning algorithms.…”

Section: Performance Comparison Analysis Of Algorithms [22]mentioning

confidence: 99%

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Pan

Lin

2020

International Journal of Digital Multimedia Broadcasting

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“…The deep learning revolution has led many to apply machine learning methods to classical problems in all fields including statistics. Examples range from estimation [1][2][3][4][5][6] to detection [7][8][9][10][11][12]. Deep learning is a promising approach for deriving high accuracy and low complexity alternatives when classical solutions are intractable.…”

Section: Introductionmentioning

confidence: 99%

“…An important ingredient of these works is the use of an artificial training set where real noise data is augmented with synthetically planted targets. In the context of radar detection, SVMs were considered in [10]. Specific CFAR radar detectors were developed by relying on CFAR features [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Learning to Detect with Constant False Alarm Rate

Diskin¹,

Okun²,

Wiesel³

2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical modelbased solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually provide high accuracy with low complexity but do not have a constant false alarm rate (CFAR) as required in many applications. To close this gap, we propose to add a term to the loss function that promotes similar distributions of the detector under any null hypothesis scenario. Experiments show that our approach leads to near CFAR detectors with similar accuracy as their competitors.

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“…It is proved that the machine learning model using the minimum mean square error loss function could be used as the Neyman-Pearson (NP) criterion [34]. In [11], a cost-sensitive support vector machine (2C-SVM) is proposed, which is an SVM with different penalty factors for different classification categories. When processing the simulated signal of Swerling I and II target and additive white Gaussian noise (AWGN) background, this detector has good performances when detecting.…”

Section: Introductionmentioning

confidence: 99%

False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs

Chen

Huang

et al. 2021

IEEE Sensors J.

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Due to the influence of the complex marine environment, the marine target detection based on statistical theory is difficult to achieve high-performance. Moreover, due to various targets' motion characteristics, only using a single feature for detection is unreliable. In this paper, from the perspective of feature extraction and classification, marine target and sea clutter are classified by deep learning methods. To achieve the required false alarm rate, the dual-channel convolutional neural networks (DCCNN) and false-alarm-controllable classifier (FACC)-based marine target detection method is proposed. Firstly, the measured sea clutter and the target signal are preprocessed to obtain the time-Doppler spectrum and amplitude information. The Marine-DCCNN (MDCCNN) is then constructed for features extraction and fusion, and the feature vectors of the signals are obtained. The performance of different feature extraction models is tested and compared. Finally, the FACC is used as a detector to classify the feature vectors into two categories and the control of the false alarm rate is realized. The detection performances were verified by two popular public radar datasets, i.e., IPIX radar dataset (floating target) and CSIR dataset (maneuvering marine target). The results show that compared with single-channel CNN and histogram of oriented gradient support vector machine (Hog-SVM) classification, a combination of MDCCNN feature extraction model and softmax classifier can achieve higher performance and controllable false alarm rate. Moreover, HH polarization and mixed training datasets under different sea states can help improve detection performance. Index Terms-Radar target detection; marine target; feature extraction; dual-channel convolutional neural network (DCCNN); false alarm controllable classifier (FACC)

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Approximating the Neyman–Pearson detector with 2C-SVMs. Application to radar detection

Cited by 14 publications

References 28 publications

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

Learning to Detect with Constant False Alarm Rate

False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs

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