RaSSteR: Random Sparse Step-Frequency Radar

TL;DR

RaSSteR introduces a sparse, random stepped-frequency radar waveform that achieves high resolution and Doppler estimation with fewer spectral resources, outperforming traditional methods especially under interference.

Contribution

It presents a novel sparse, random stepped-frequency waveform that reduces spectral resource usage while maintaining resolution and improving interference robustness.

Findings

RaSSteR achieves identical recovery guarantees with fewer carriers.

Performance exceeds state-of-the-art methods like SFW and RSF.

Target hit rate improves by 30% under interference.

Abstract

We propose a method for synthesizing high range resolution profiles (HRRP) using stepped frequency waveform (SFW) processing. Conventional SFW radars sweep over the available spectrum linearly to achieve high resolution from their instantaneous bandwidth. However, they suffer from strong range-Doppler coupling and coexisting spectral interference. Prior works are able to mitigate only one of these drawbacks. We present a new \textit{ra}ndom \textit{s}parse \textit{ste}p-frequency \textit{r}adar (RaSSteR) waveform that consumes less spectral resources without loss of range resolution and estimates both high-resolution range and Doppler by exploiting sparse recovery techniques. In the presence of interference, the operation with the new waveform is made cognitive by focusing available transmit power only in the few transmit bands. Our theoretical analyses show that, even while using fewer carriers in the available bandwidth, RaSSteR has identical recovery guarantees as the standard random stepped frequency (RSF) waveform. Numerical experiments demonstrate performance enhancements with RaSSteR over state-of-the-art such as SFW, RSF, conventional pulse-compression-based pulse Doppler radar, and sub-Nyquist radar. In addition, the target hit rate of RaSSteR in the presence of strong interference is 30% more than conventional RSF.