Deterministic Cramer-Rao bound for strictly non-circular sources and analytical analysis of the achievable gains
Jens Steinwandt, Florian Roemer, Martin Haardt, Giovanni Del Galdo

TL;DR
This paper derives the deterministic Cramer-Rao bound for strictly non-circular sources, analyzes the potential estimation gains, and provides simplified expressions and conditions for maximum achievable NC gain in specific scenarios.
Contribution
It introduces a closed-form deterministic R-D NC CRB for multi-dimensional parameter estimation of non-circular sources and analyzes the achievable NC gain analytically.
Findings
In some cases, no NC gain is achievable.
Simplified CRB expressions for two closely-spaced sources.
Conditions for maximum NC gain depending on physical parameters.
Abstract
Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. In this paper, as a benchmark for these NC methods, we derive the closed-form deterministic R-D NC Cramer-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources. Assuming a separable centro-symmetric R-D array, we show that in some special cases, the deterministic R-D NC CRB reduces to the existing deterministic R-D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved in these cases. For more general scenarios, finding an analytical expression of…
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.
