DOA Estimation Methods and Algorithms

Chung, Pei-Jung; Viberg, Mats; Yu, Jia Yuan

doi:10.1016/b978-0-12-411597-2.00014-x

Cited by 55 publications

(32 citation statements)

References 166 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The concept of sparse representation was later extended within the framework of compressed sensing [13][14][15]. In compressed sensing, a sparse signal, represented by the sparse vector x, is recovered from undersampled linear measurements y, i.e., the system model (16) applies with M N . In this context, y is referred to as the compressive data, A is the sensing matrix, and e denotes the measurement noise.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this context, y is referred to as the compressive data, A is the sensing matrix, and e denotes the measurement noise. Note that a data model similar to (16) applies if the signal of interest is sparse in some domain. Given y and A, the problem of sparse signal recovery in compressed sensing is also to solve for the sparse vector x subject to data consistency.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Gaussian noise with variance σ. It follows from the data model in (16) that y follows a Gaussian distribution with mean zero and covariance R = AP A H + σI. Consequently, the negative log-likelihood function associated with y is given by…”

Section: Maximum Likelihood Estimation (Mle)mentioning

confidence: 99%

See 2 more Smart Citations

Sparse methods for direction-of-arrival estimation

Yang

Stoica

et al. 2018

Academic Press Library in Signal Processing, Volume 7

209

139

View full text Add to dashboard Cite

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Sparse methods for direction-of-arrival estimation

Yang

Stoica

et al. 2018

Academic Press Library in Signal Processing, Volume 7

209

139

View full text Add to dashboard Cite

“…In a special case when W = I N , the formulation in (15) can be shown to be equivalent to the MUSIC estimator [19]. • Covariance Fitting: Starting from the identity in (4) and applying the least squares covariance fitting without considering the weighting matrix W , we obtain [27]:…”

Section: Existing Methods Based On Non-linear Lsmentioning

confidence: 99%

“…The application of DOA estimation spans multiple fields of research, including wireless communication, radio astronomy, automotive radar, etc. [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework

Trinh-Hoang

Viberg

Pesavento

2018

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

In this paper, the partial relaxation approach is introduced and applied to the DOA estimation problem using spectral search. Unlike existing spectral-based methods like conventional beamformer, Capon beamformer or MUSIC which can be considered as single source approximation of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each considered direction, the manifold structure of the remaining interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the "interference" parameters. Thanks to this relaxation, the conventional multi-source optimization problem reduces to a simple spectral search. Following this principle, we propose estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and covariance fitting methods. To calculate the null-spectra efficiently, an iterative rooting scheme based on the rational function approximation is applied to the partial relaxation methods. Simulation results show that, irrespectively of any specific structure of the sensor array, the performance of the proposed estimators is superior to the conventional methods, especially in the case of low Signal-to-Noise-Ratio and low number of snapshots, while maintaining a computational cost which is comparable to MUSIC.

show abstract

Unmanned aircraft flight control aided by phased‐array radio navigation

Gryte

Bryne

Johansen

2020

Journal of Field Robotics

View full text Add to dashboard Cite

Navigation systems of safety-critical unmanned aircraft need an alternative position aiding source to global navigation satellite system. One promising alternative, is position measurements from phased-array radio systems. The presented navigation system consist of a multiplicative extended Kalman filter that utilize these measurements, along with an exogenous altitude measurement, to aid an inertial navigation system. The reliability and accuracy of the navigation system is demonstrated through beyond-visual-line-of-sight flights with a fixed-wing unmanned aerial vehicle, without the use of global navigation satellite systems. This uses a conventional guidance controller module, which also monitor the navigation quality.

show abstract

DOA Estimation Methods and Algorithms

Cited by 55 publications

References 166 publications

Sparse methods for direction-of-arrival estimation

Sparse methods for direction-of-arrival estimation

Partial Relaxation Approach: An Eigenvalue-Based DOA Estimator Framework

Unmanned aircraft flight control aided by phased‐array radio navigation

Contact Info

Product

Resources

About