Research on millimeter-wave radar target detection in complex security environment

Millimeter-wave radar is widely used in various security scenarios with the advantages of small size, light weight, high spatial resolution, all-weather, all-day work, but in response to complex environments such as wind, rain, always produce a certain number of false alarms, if the detection threshold is increased, it will cause missed detection. Aiming at the false alarm problem of millimeter-wave radar in complex security environment, an algorithm for filtering false alarm targets is proposed. Firstly, the ADC data of millimeter-wave radar in the actual complex environment is collected, the phasor mean cancellation algorithm is used to suppress the stationary target, and the signal-to-noise ratio of the moving target is improved, and then the data after the Fourier transform (2D-FFT) of the distance dimension and velocity dimension are accumulated non-coherently, the distance and velocity information of the target is extracted by the two-dimensional unit average constant false alarm detection algorithm, and the angle of the target is extracted based on the angle dimension Fourier transform, and the point target is converted into a two-dimensional plane point cloud. A velocity-based density clustering algorithm is proposed to separate clutter and intrusion targets, filter false alarm targets, and reduce false alarms. The experimental results show that the filtering rate of the proposed false alarm filtering algorithm is more than 85% when it is windy and rainy, and when there are intruding targets at the same time.