Zhejiang University

Donghai Laboratory

Yuying Song

Zhiwei Xu

Chunyi Song

Jingxuan Wu

Fuyuan Ai

Zhihui Cao

The embodiment of the present disclosure provides a deep neural network (DNN)-based multi-target constant false alarm rate (CFAR) detection method. The method includes: obtaining target values to be measured based on radar IF (IF) signals to be detected, the target values to be measured including a measured frequency value and a measured intensity value of the radar IF signals; obtaining peak sequences based on the target values to be measured; generating a target detection result by processing the peak sequences based on a DNN detector, the DNN detector being a machine learning model; generating approximated maximum likelihood estimation (AMLE) of a scale parameter based on an approximated maximum likelihood estimator; generating a false alarm adjustment threshold based on a preset false alarm rate and the AMLE; and generating a constant false alarm detection result by processing the target detection result based on the false alarm adjustment threshold.

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Zhoushan, China

Yuying Song