FOMP algorithm for Direction of Arrival estimation

Dehghani, Maryam; Aghababaiyan, Keyvan

doi:10.1016/j.phycom.2017.12.012

Cited by 45 publications

(29 citation statements)

References 19 publications

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“…where (•) j denotes the j-th iteration. Verifying that the updates (16)- (19) give a non-decreasing sequence ln p(W, j , τ j , ε j , ρ j ) is easy (see Appendix A). From [22], [27], p(X|W, , τ , ε, ρ) is complex Gaussian:…”

Section: B Bayesian Inferencementioning

confidence: 99%

Sparse Bayesian Approach for DOD and DOA Estimation With Bistatic MIMO Radar

2019

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This study addresses the problem of joint direction-of-departure (DOD) and direction-ofarrival (DOA) estimation with bistatic multiple-input multiple-output (MIMO) radar. To the best of our knowledge, a limited number of sparse Bayesian learning (SBL)-based methods exist that can be applied to joint DOD and DOA estimation. This is because of the heavy computational load and strong correlation between the nearby basis. To overcome these challenges, we present a new coarse non-uniformly sampled 2D grid and propose an improved SBL-based method for joint estimation of the DOD and DOA in MIMO radar. With the new grid, the computational load can be significantly reduced, and the nearby 2D grid points can provide a low correlation basis. To handle the modeling error derived from the coarse grid, we also introduce a modified linear approximation method into the SBL framework in which the locations of grid points are considered as adjustable parameters, and the grid points can be updated recursively. Finally, a block majorization-minimization algorithm is applied to perform Bayesian inference. Experimental results indicate that our method can improve the joint DOD and DOA estimation performance, particularly in the case of low signal-noise-ratio, limited snapshots, or correlated signals. INDEX TERMS DOD and DOA estimation, bistatic MIMO radar, sparse Bayesian learning, majorization-minimization algorithm, coarse non-uniformly sampled 2D grid.

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Section: B Bayesian Inferencementioning

confidence: 99%

Sparse Bayesian Approach for DOD and DOA Estimation With Bistatic MIMO Radar

2019

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“…It is worth mentioning that TDOA-based source bearing estimation would be limited to a single source scenario. The spatial spectral beamformers on the other hand can localize more than one source, and even may distinguish between DOAs when the number of signal sources is more than the number of antennas by using compressive sensing [22].…”

Section: Tdoa-based Bearing Estimation Problemmentioning

confidence: 99%

A Measurement-Division Model for TDOA-Based Source Bearing Estimation With Unknown Propagation Speed

Cui

Zhao

2019

IEEE Access

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This paper focuses on the bearing estimation problem of far-field signal source via time-difference-of-arrival (TDOA) with a synchronized array in 3-D space. It is usually assumed that the propagation speed (PS) is perfectly known in localization. In reality, only an imperfect knowledge of PS could be obtained. The traditional closed-form solutions without involving PS have the advantage of low complexity, but suffer from low estimation accuracy. A measurement-division model is proposed to offer an alternative solution without the need of the propagation speed. This speed-free model combines two original TDOA measurement equations into a division formula, whose Cramer-Rao lower bound (CRLB) is derived for the observed data. A typical optimization method, i.e. the Levenberg-Marquardt (LM) algorithm is adopted to resolve the nonlinear measurement-division model, resulting in an estimation accuracy improvement because of its iterative search behavior. The theoretical performance of this solution is evaluated in terms of bias and covariance. Simulations are conducted to demonstrate an accuracy advantage of the solution over the related methods.

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“…The matching pursuit [7] and orthogonal matching pursuit (OMP) [8] belong to the greedy algorithms. In recent researches, [9] offers an improved version of OMP algorithm called focused orthogonal matching pursuit (FOMP), which is able to distinguish between closely spaced DOAs. Furthermore, the FOMP algorithm is extended to three‐dimensional scenario [10] and shows good estimation performance.…”

Section: Introductionmentioning

confidence: 99%