Joint Angle and Frequency Estimation with Uniform Eigenvalue Weighting
Tuo Fu, Jiee Chen, Xiqi Gao · 2006
In this paper, we address the problem of joint angle and frequency estimation based on uniform linear array (ULA). We derive a new version of ESPRIT-type algorithm to solve this problem. The proposed method follows the classic data stacking and real processing techniques for signal subspace estimation. We transform the estimated real signal subspace back to the complex domain. The purpose of this transformation is to avoid a potential bad-conditioned invariance equation and unbalanced weighting for the joint eigenvalue estimation. Then the complex invariance equation is formed and solved. By such a procedure, we show that the simple joint eigenvalue estimation method can still be applied after the complex invariance equation is solved. By the more sophistic and reliable joint eigenvalue estimation technique, we can obtain a uniform weighting for this estimation. Numeral simulations validate the proposed algorithms.