Expected likelihood for compressive sensing-based DOA estimation

Bilik, Igal; Northardt, Tom; Abramovich, Y.I.

doi:10.1049/cp.2012.1710

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the compressive sensing theory can be applied to the problem of DOA estimation by splitting the angular region into N potential DOAs, where only s N of the DOAs have an impinging signal (alternatively N − s of the angular directions have a zero-valued impinging signal present) [107], [108]. These DOAs are then estimated by finding the minimum number of DOAs with a non-zero valued impinging signal that still gives an acceptable estimate of the array output [23], [104].…”

Section: ) Direction-of-arrival (Doa)mentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

View full text Add to dashboard Cite

Nowadays, a large amount of information has to be transmitted or processed. This implies highpower processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.

show abstract

Section: ) Direction-of-arrival (Doa)mentioning

confidence: 99%

A Review of Sparse Recovery Algorithms

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For the derivation of ( 18) and (19) please see Appendix C. The maximisation is then achieved by iteratively finding Σ and µ, followed by p new k,n for n = 1, ..., N and σ 2 k,new until a convergence criterion is met [16], [17]. In other words, the new estimates for the noise variance and precision hyperparameters found from (19) and (18) are then used in (14) and (15) to find new estimates of the covariance matrix and mean of the distribution in (13). Note that when xp = [0, 0, ..., 0] T the update expressions match those used by the traditional RVM.…”

Section: B Modified Relevance Vector Machine For Doa Estimationmentioning

confidence: 99%

Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation With Mutual Coupling Effects

Hawes

Mihaylova

Septier

et al. 2017

IEEE Trans. Antennas Propagat.

View full text Add to dashboard Cite

The problem of estimating the dynamic direction of arrival of far field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This work proposes two novel approaches able to provide accurate solutions, including at the endfire regions of the array. Firstly, a Bayesian compressive sensing Kalman filter is developed, which accounts for the predicted estimated signals rather than using the traditional sparse prior. The posterior probability density function of the received source signals and the expression for the related marginal likelihood function are derived theoretically. Next, a Gibbs sampling based approach with indicator variables in the sparsity prior is developed. This allows sparsity to be explicitly enforced in different ways, including when an angle is too far from the previous estimate. The proposed approaches are validated and evaluated over different test scenarios and compared to the traditional relevance vector machine based method. An improved accuracy in terms of average root mean square error values is achieved (up to 73.39% for the modified relevance vector machine based approach and 86.36% for the Gibbs sampling based approach). The proposed approaches prove to be particularly useful for direction of arrival estimation when the angle of arrival moves into the endfire region of the array.

show abstract

Expected likelihood for compressive sensing-based DOA estimation

Cited by 2 publications

References 0 publications

A Review of Sparse Recovery Algorithms

A Review of Sparse Recovery Algorithms

Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation With Mutual Coupling Effects

Contact Info

Product

Resources

About