DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning

Lin, Chia-Hung; Lin, Yu-Chien; Bai, Yue; Chung, Wei-Ho; Lee, Ta-Sung; Huttunen, Heikki

doi:10.1109/vtcfall.2019.8891420

Cited by 22 publications

(13 citation statements)

References 12 publications

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“…• NET: a learned neural network as in Algorithm 1. We choose a uniform fake prior for the unknown parameters in (15). The architecture is based on four non-linear features: the sample mean of x, its sample variance and robust versions of the two based on the median.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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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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Section: Numerical Experimentsmentioning

confidence: 99%

“…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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“…As the neural network (NN) has been the main focus of interest for researchers in recent years, there have been several trials to construct an NN architecture-based target detector and to obtain a better receiver operating characteristic (ROC) than that of traditional CFAR techniques [5][6][7][8][9][10][11]. Among them, [5][6][7][8] proposed using multi-layer perceptron (MLP)-based architectures either to construct a target detector or to identify the noise background.…”

Section: Introductionmentioning

confidence: 99%

“…Akhtar and Olsen [7,8] trained the MLPbased detectors using the results of CA-CFAR and GO-CFAR. Convolutional neural network (CNN)-based architectures were also suggested in [9][10][11]. Lin et al [9] trained a network to estimate the noise variance more accurately, even when the target existed in the range-Doppler domain.…”

Section: Introductionmentioning

confidence: 99%

“…Convolutional neural network (CNN)-based architectures were also suggested in [9][10][11]. Lin et al [9] trained a network to estimate the noise variance more accurately, even when the target existed in the range-Doppler domain. The authors in [10] trained the CNN model for target detection in the range-Doppler domain, but their NN determined the target presence or absence under a single range-Doppler map.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Target Detector Using Maxpooling and Hadamard Division Layers in FM-Band Passive Coherent Location

Park

Kim

2022

J Electromagn Eng Sci

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The constant false alarm rate (CFAR) has been widely used in radar systems to detect target echo signals because of its simplicity. With the recent development of different types of neural networks (NNs), NN architecture-based target detection methods are also being considered. Several studies related to NN-based target detectors have introduced multi-layer perceptron-based and convolutional neural network (CNN)-based structures. In this paper, we propose a CNN-based target detection method in frequency modulation (FM)-band passive coherent location (PCL). We improved the detection performance using a maxpooling layer and a Hadamard division layer, which are parallelly placed with a CNN layer. Moreover, in our method there is no need to determine the specific cell configuration (e.g., cell under test, reference cells, and guard cells) because the proposed method obtains the trained kernels by end-to-end learning. We show that the trained kernels help in the extraction of either signal or noise components. Through the simulations, we also prove that the proposed method can yield an improved receiver operating characteristic compared to that of a cell-averaging CFAR detector for FM-band PCL in a homogeneous environment.

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A Survey on Deep Learning-Based Vehicular Communication Applications

Lin

et al. 2020

J Sign Process Syst

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DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning

Cited by 22 publications

References 12 publications

Learning to Detect with Constant False Alarm Rate

Learning to Detect with Constant False Alarm Rate

Convolutional Neural Network-Based Target Detector Using Maxpooling and Hadamard Division Layers in FM-Band Passive Coherent Location

A Survey on Deep Learning-Based Vehicular Communication Applications

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