Stepped Frequency Pulse Compression With Noncoherent Radar Using Deep Learning

A deep neural network (DNN) is used for achieving subpulse resolution in noncoherent stepped frequency waveform radar. The tradeoff between high and long range radar systems often addressed using pulse compression, allowing both pulses by increasing the bandwidth. This typically requires a coherent In this article we present learning-based solution with Our results such system are comparable to an equivalent signal-to-noise ratios (SNRs) greater than 10 dB. All based on simulated data.