Performance analysis of subspace‐based spatio‐temporal spectral estimator

• Published in: Transactions on emerging telecommunications technologies 2022-11, Vol.33 (11), p.n/a
• Main Authors: Amirsoleimani, Shervin, Olfat, Ali
• Format: Article
• Language: English
• ISSN: 2161-3915; 2161-3915
• DOI: 10.1002/ett.4576

In this article, the performance analysis of subspace‐based spatial‐temporal spectral estimation is investigated. Subspace methods adopt an eigenstructure approach to estimate the array observation power in the frequency‐direction (f−θ$$ f-\theta $$) plane. This approach is used in many wideband array processing techniques using spatial‐temporal covariance matrix (STCM). In this article, the performance of subspace‐based algorithms is addressed in case of two plane waves, separated in direction of arrival (DOA) and frequency, impinging on a linear array. The analysis on one‐dimensional (f$$ f $$ or θ$$ \theta $$) subspace‐based methods are extended to the two‐dimensional case (f−θ$$ f-\theta $$). Then, an analytic formulation for the expected value of the null spectrum of the observations' spatial‐temporal spectral is presented. Accordingly, two resolution criteria for resolving two plane waves are discussed, and corresponding analytic relations are derived and verified through simulations. In this analysis, the effect of temporal lag order, number of snapshots, and signal‐to‐noise ratio (SNR) in DOA and frequency resolution are well shown. Finally, two conceptual examples of practical design scenarios are presented. In this article, the performance of subspace‐based algorithms is addressed in case of two plane waves, separated in direction of arrival and frequency, impinging on a linear array. The analysis on one‐dimensional subspace‐based methods is extended to the two‐dimensional case.