Stepped Frequency Pulse Compression with Non-Coherent Radar using Deep Learning

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

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Deep learning

frequency-agile radar

noncoherent radar

pulse compression

supervised learning

Bandwidth compression

Deep neural networks

Economic and social effects

Holography

Radar signal processing

Coherent radar

Coherent system

High resolution

Long pulse

Stepped frequency

Stepped frequency waveforms

Subpulse

Trade off

Publication and Content Type

ref (subject category)

art (subject category)