A New CFAR Detection Algorithm Based on Sorting Selection for Vehicle Millimeter Wave Radar

Chen, Ruida; Jiang, Yicheng; Miao, Zhenwei; Yu, Gang; Wang, Bing

doi:10.4271/2020-01-5019

SAE Technical Paper Series 2020

DOI: 10.4271/2020-01-5019

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A New CFAR Detection Algorithm Based on Sorting Selection for Vehicle Millimeter Wave Radar

Ruida Chen¹,

Yicheng Jiang²,

Zhenwei Miao³

et al.

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Cited by 2 publications

References 15 publications

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Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

Dong,

Zhang

2024

Drones

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With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

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Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

Dong,

Zhang

2024

Drones

View full text Add to dashboard Cite

show abstract

A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging

Wang

Huo

et al. 2023

JMSE

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Due to the influence of environmental noise, sidelobe data, and tunnel emission under the background of multi-background underwater surveying and mapping, it is challenging to detect seafloor terrain in the background noise. Constant false alarm detection of seafloor terrain under the condition of constant false alarm probability has been an important research field. The constant false alarm detection can eliminate noise interference in a water body in the seabed topography mapping process and provide clear and accurate seabed topography information. Therefore, it is a challenging task to increase the detection probability, reduce the missing probability, and increase the detection speed in constant false alarm detection methods. Aiming at the shortcomings of the existing algorithms, this paper proposes an efficient weighted cell averaged constant false alarm detection method (WCA-CFAR). First, the cross-window reference unit sampling method is used to improve the detection speed and accurately sample the background noise unit. Then, the reference unit weighted average constant false alarm detection method is employed to calculate the detection threshold to achieve the purpose of target detection. The proposed method is verified by the simulation data detection test and a test on the actual lake test data. The test results show that the proposed method can effectively reduce the missing detection probability and improve the detection probability.

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A New CFAR Detection Algorithm Based on Sorting Selection for Vehicle Millimeter Wave Radar

Cited by 2 publications

References 15 publications

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging

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