Application of Sparse Representation of Complex Data in Railway Positioning and Collision Alert Systems Using Millimeter-Wave Radar
摘要
Abstract
The paper presents the results from the experimental study of a modified artificial neural network MFNN (minimum fuel neural network). Sparse representation of complex data with overcomplete basis and L0/L1 norm optimization are used instead of the classical fast Fourier transform (FFT) algorithm. The results showed a significant enhancement in the ability of obstacle recognition and autonomous railway control systems to distinguish between close objects such as trains on adjacent tracks at marshalling yards.