Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Study of Multi-Step Knowledge-Aided Iterative Nested MUSIC for Direction Finding
In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation applied to the signals impinging on a two-level nested array, referred to as multi-step knowledge-aided iterative nested MUSIC…
Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms
In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowl…
Rydberg Atomic Quantum Receivers for Multi-Target DOA Estimation
Quantum sensing technologies have experienced rapid progresses since entering the `second quantum revolution'. Among various candidates, schemes relying on Rydberg atoms exhibit compelling advantages for detecting radio …
Beamspace Multidimensional ESPRIT Approaches for Simultaneous Localization and Communications
Modern wireless communication systems operating at high carrier frequencies are characterized by a high dimensionality of the underlying parameter space (including channel gains, angles, delays, and possibly Doppler shif…
Two-step Machine Learning Approach for Channel Estimation with Mixed Resolution RF Chains
Massive MIMO is one of the main features of 5G mobile radio systems. However, it often leads to high cost, size and power consumption. To overcome these issues, the use of constrained radio frequency (RF) frontends has b…
BIG-bench Machine LearningGenerative Adversarial Network