The application provides a weak
radar cross section unmanned aerial vehicle
radar target detection method and
system based on
frequency modulation continuous wave radar, belongs to the field of
radar signal processing and intelligent sensing, and aims to solve the detection problem of low-altitude weak reflection targets caused by weak echo energy, serious
multipath effect and
clutter interference. The method synchronously acquires and spatiotemporally calibrates the real-time dynamic differential positioning information of the unmanned aerial vehicle and the radar, automatically generates high-precision labels for
supervised learning, and reduces the artificial labeling cost and error. After two-dimensional
fast Fourier transform is performed on the radar echo, an adaptive non-uniform compression strategy is adopted to aggregate key timing features to reduce
data redundancy. Further, a spatiotemporal feature decoupling enhancement module is used to suppress multipath
ambiguity and improve feature separability. Finally, a target detection network based on an
encoder-decoder
noise reduction reconstruction is constructed to denoise and finely reconstruct the features, so that the weak reflection unmanned aerial vehicle target can be accurately and robustly detected and positioned in a complex environment.