Theme paperUnified analysis for DOA estimation algorithms in array signal processing
References (30)
Improving the resolution performance of eigenstructure-based direction-finding algorithm
- et al.
Performance analysis of Root-MUSIC
IEEE Trans. Acoust. Speech Signal Process.
(December 1989) - et al.
Performance analysis of ESPRIT and TAM in determining the direction of arrival of plane waves in noise
IEEE Trans. Acoust. Speech Signal Process.
(December 1989) - et al.
Performance of high resolution frequencies estimation methods compared to the Cramer-Rao bounds
IEEE Trans. Acoust. Speech Signal Process.
(November 1989) - et al.
A performance analysis of the adaptive algorithms in the presence of calibration errors
- et al.
Theoretical performance prediction of the MUSIC algorithm
A sensitivity analysis of the MUSIC algorithm
- et al.
Perturbation analysis of TK method for harmonic retrieval problems
IEEE Trans. Acoust. Speech Signal Process.
(February 1988) - et al.
Matrix pencil method and its performance
- et al.
The statistical performance of the MUSIC and the Minimum-Norm algorithms in resolving plane-waves in noise
IEEE Trans. Acoust. Speech Signal Process.
(April 1986)
Estimating the angles of arrival of multiple plane waves
IEEE Trans. Aerospace Electron. Systems
State-space and singular-value decomposition-based approximation methods for the harmonic retrieval problem
J. Opt. Soc. Amer.
The Theory of Matrices
MUSIC performance prediction by matrix approximation at high SNR
Min-Norm Linear Prediction for arbitrary sensor array
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2021, Linear Algebra and Its ApplicationsPerformance analysis of G-MUSIC based DOA estimator with random linear array: A single source case
2018, Signal ProcessingCitation Excerpt :On the whole, we assume that each sensor in the RLA is in operation with a known probability p. For DOA estimation, subspace-based approaches are widely used because of their low complexity and robustness relative to the maximum-likelihood (ML) techniques, which are sensitive to signal and noise covariance matrix modeling errors [1–4]. Popular subspace-based algorithms for DOA estimation include MUltiple SIgnal Classification (MUSIC) [5–9], and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) [10].
Performance improvement of direction finding algorithms in non-homogeneous environment through data fusion
2015, Digital Signal Processing: A Review JournalCitation Excerpt :The latter are then fused on the basis of their accuracy to obtain a fused DOA using the federated fusion algorithm [21]. The DOA accuracy expression is obtained from the well-established performance analysis of DOA MUSIC-based algorithms [22–25]. The aim of this paper is to demonstrate that the proposed approach based on data fusion concepts and the well-known performance analysis can improve significantly subspace based algorithms.
Transmitter precoder design to improve the performance of the MUSIC algorithm
2013, Signal ProcessingCitation Excerpt :The MUSIC [15] algorithm is a representative subspace based algorithm. There has been considerable research work in either analyzing its performance [10,16–18,3,4] or developing more advanced robust MUSIC algorithms [1,13,5]. In a multipath environment, the MUSIC algorithm can be used to estimate the AoAs of signals impinging on the receive antenna array simultaneously [8].
Analytical Performance Assessment of 2-D Tensor ESPRIT in Terms of Physical Parameters
2024, IEEE Open Journal of Signal ProcessingPerformance Analysis of DOA Estimation Algorithms Using Physical Parameters in Specific Cases
2023, IEEE Workshop on Statistical Signal Processing Proceedings