Classification of UAV-to-Ground Targets Based on Enhanced Micro-Doppler Features Extracted via PCA and Compressed Sensing

In order to achieve precise operations on specified targets from the unmanned aerial vehicles (UAVs), classifying ground targets correctly is especially important. Micro-Doppler effect which provides unique information of targets has been the basis for targets classification. Due to the effect of ground clutter, noise and complex signal modulation, enhancing micro-Doppler features of UAV-to-ground targets is necessary for accurate classification. This paper firstly establishes the models of UAV-to-ground targets including wheeled vehicles, tracked vehicles and pedestrians to analyze their micro-Doppler differences. Secondly, Principal Components Analysis (PCA) is utilized to remove the ground clutter. Compared with other algorithms, PCA can use a small amount of calculation to remove the ground clutter while retain nearby micro-Doppler signals. Then, micro-Doppler signals are sparsely represented based on Fourier basis. Orthogonal Matching Pursuit (OMP) is chosen to reconstruct micro-Doppler components and refine spectral lines after random projection. The three steps make up Compressed Sensing (CS) together. At last, non-linear transform of Doppler spectrum is conducted to further enhance the distinction of micro-Doppler spectral lines. Distinguishing micro-Doppler features are extracted from pre-processed micro-Doppler signals, which eventually contributes to the accurate targets classification. Comparison with other methods is also made to prove the robustness and anti-noise performance of proposed method.