A General Sequential Delay-Doppler Estimation Scheme for Sub-Nyquist Pulse-Doppler Radar
Shengyao Chen, Feng Xi, Zhong Liu

TL;DR
This paper introduces a versatile sequential delay-Doppler estimation method for sub-Nyquist pulse-Doppler radar that works for all AIC systems and targets both on and off the grid, using spectrum estimation techniques.
Contribution
It presents a general estimation framework applicable to all AICs, with conditions for off-grid target estimation, improving upon prior AIC-dependent methods.
Findings
Effective high-resolution delay-Doppler estimates for off-grid targets.
Applicable to all AIC systems regardless of target grid position.
Theoretical guarantees for successful off-grid target estimation.
Abstract
Sequential estimation of the delay and Doppler parameters for sub-Nyquist radars by analog-to-information conversion (AIC) systems has received wide attention recently. However, the estimation methods reported are AIC-dependent and have poor performance for off-grid targets. This paper develops a general estimation scheme in the sense that it is applicable to all AICs regardless whether the targets are on or off the grids. The proposed scheme estimates the delay and Doppler parameters sequentially, in which the delay estimation is formulated into a beamspace direction-of- arrival problem and the Doppler estimation is translated into a line spectrum estimation problem. Then the well-known spatial and temporal spectrum estimation techniques are used to provide efficient and high-resolution estimates of the delay and Doppler parameters. In addition, sufficient conditions on the AIC to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRadar Systems and Signal Processing · Direction-of-Arrival Estimation Techniques · Sparse and Compressive Sensing Techniques
