Radar Signal Decomposition in Steering Vector Space for Multi-Target Classification

In automotive frequency-modulated continuous wave radar systems, targets with similar ranges and velocities might be detected as a single target because their target information overlaps on the range-velocity (RV) plane. When the information of multiple targets overlaps on the RV plane, target classification cannot be performed accurately. Therefore, we propose a method to decompose the overlapped target information on the RV plane for effective target classification. To separate each target's radar signal, we use an orthogonal projection, in which the steering vector formed from the angle information of the desired target is projected onto the kernel space composed of other steering vectors. To verify the performance of the proposed method, a convolutional neural network-based target classification is performed on the decomposed radar signals. Compared to the angle-fast Fourier transform (FFT)-based signal decomposition, the orthogonal projection-based signal decomposition shows higher classification accuracy, which implies that the target information overlapped on the RV plane is effectively separated. When information of two targets is overlapped, the classification accuracy of the proposed method is 5.9%p higher than that of the conventional method. Moreover, when the information of three targets is overlapped, the accuracy is improved by 25.5%p.