Research on intelligent target classification algorithm for water flow velocity radar

Flow velocity measurement is an important part in hydrological monitoring activities. Aiming at the problems of low accuracy and long time of velocity measurement in current flow velocity radar, we design a millimeter wave cross-sectional flow velocity radar system by extracting water flow echo for velocity measurement, and propose an Enhanced Squeeze-and-Excitation Network (ESE-Net) which is used for the intelligent target classification. The structure uses a multi-scale wide residual network to broaden the network width and enhance the representation ability of the network, while avoiding the degradation problem caused by network deepening. The attention mechanism is introduced into the network model to strengthen the role of informative features in the network, and realize the recognition and classification of three types of targets, water flow, floater and land. Meanwhile, the hyper-parameters of this network are optimized using the manta ray foraging optimization algorithm (dFDB-MRFO), and the recognition effect was further improved.