DIAT-RadSATNet—A Novel Lightweight DCNN Architecture for Micro-Doppler-Based Small Unmanned Aerial Vehicle (SUAV) Targets’ Detection and Classification

Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture. In this work, a lightweight novel DCNN model (named “DIAT-RadSATNet”) is designed for the accurate SUAV targets: RC plane, three-short-blade rotor, three-long-blade rotor, quadcopter, bionic bird, and mini-helicopter + bionic bird; and detection/classification based on their m-D signatures. A diversified,
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-band (10 GHz) continuous-wave (CW) radar-based, open-field-collected m-D signatures dataset (named “DIAT-
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SAT”) is used for the design/testing of “DIAT-RadSATNet.” A set of new design principles is proposed through multifactors: layers, #parameters, floating-point operations (FLOPs), number of blocks, filter dimension, memory size, number of parallel paths, and accuracy; optimization is applied via a series of in-depth ablation studies. The novel “DIAT-RadSATNet” module consists of 0.45 M trainable parameters, 40 layers, 2.21-Mb memory size, 0.59G FLOPs, and 0.21-s computation-time complexity. The detection/classification accuracy of “DIAT-RadSATNet,” based on the open-field unknown dataset experiments, falls within 97.1% and 97.3%. A comparative analysis with state-of-the-art (SOTA) DCNN models evidences the betterness, more efficiency, and more accuracy of our novel “DIAT-RadSATNet” architecture.