Comparison of CFAR Methods on Multiple Targets in Sea Clutter Using SPX-Radar-Simulator

Sahal, Mochammad; Said, Zaidan Adenin; Putra, Rifky Yulianto; Kadir, Rusdhianto Effendi Abdul; Firmansyah, Adrian Aryaputra

doi:10.1109/isitia49792.2020.9163697

Cited by 14 publications

(2 citation statements)

References 15 publications

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“…In real environments, the effects of interference, noise or background returned from the terrain are not constant in frequency [18]. Therefore, CA-CFAR detection [19] is applied to R-D maps rather than fixed thresholds. For a given frame, target locations are targeted for clustering, including point-shaped velocity profiles and extended distance profiles [20].…”

Section: Data Pre-processingmentioning

confidence: 99%

Vehicle classification applying many‐to‐one input network architecture in 77‐GHz FMCW radar

Jiang

Zhang

Wang

et al. 2021

IET Radar Sonar & Navi

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In this paper, we proposed a Many-to-One Input Network Architecture (MOINA) for the classification of similar structured vehicles (bus, truck and car). The inputs of the architecture are the multiple-masked region-of-interest of the same detected vehicle from Range-Doppler maps, which are acquired by FMCW radar. The proposed method is trained with a supervised system yielding a classification accuracy of 98%. MOINA shows good classification performance in a practical situation. Besides, the F1-score of buses, trucks and cars are 98.7%, 98.0% and 97.6%, respectively.

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Section: Data Pre-processingmentioning

confidence: 99%

Vehicle classification applying many‐to‐one input network architecture in 77‐GHz FMCW radar

Jiang

Zhang

Wang

et al. 2021

IET Radar Sonar & Navi

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show abstract

“…Double parameter constant false alarm rate (DP-CFAR) is a kind of constant false alarm probability algorithm. The DP-CFAR algorithm [26,27] refers to the use of mathematical statistics theory to estimate the parameters of the detection model while keeping the target false alarm rate unchanged, which not only reduces the calculation amount of the algorithm, but also adaptively adjusts the threshold in complex background environment.…”

Section: Dp-cfar Detectionmentioning

confidence: 99%

A Novel SAR Image Target Recognition Algorithm under Big Data Analysis

Chen

Wang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Synthetic aperture radar (SAR) image target recognition technology is aimed at automatically determining the presence or absence of target information from the input SAR image and improving the efficiency and accuracy of SAR image interpretation. Based on big data analysis, dirty data is removed, clean data is returned, and standardized processing of SAR image data is realized. At the same time, by establishing a statistical model of coherent speckles, the convolutional autoencoder is used to denoise the SAR image. Finally, the network model modified by softmax cross-entropy loss and Fisher loss is used for automatic target recognition. Based on the MSTAR data set, two scene graphs containing the target synthesized by the background image and the target slice are used for experiments. Several comparative experiments have verified the effectiveness of the classification and recognition model in this paper.

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CFAR-Based Point Cloud Extraction Method for Circular Scanning Ground-Based SAR 3D Image

Wang

Zhi

Shen

et al. 2022

2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)

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Comparison of CFAR Methods on Multiple Targets in Sea Clutter Using SPX-Radar-Simulator

Cited by 14 publications

References 15 publications

Vehicle classification applying many‐to‐one input network architecture in 77‐GHz FMCW radar

Vehicle classification applying many‐to‐one input network architecture in 77‐GHz FMCW radar

A Novel SAR Image Target Recognition Algorithm under Big Data Analysis

CFAR-Based Point Cloud Extraction Method for Circular Scanning Ground-Based SAR 3D Image

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