Temporal and spatial sampling influence on the estimates of superimposed narrowband signals: when less can mean more

Chambers, Christopher P.; Tozer, T.C.; Sharman, Ken; Durrani, T.S.

doi:10.1109/78.553482

Cited by 50 publications

(28 citation statements)

References 29 publications

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“…It is clear from (2), that the dominant factors responsible for the estimation performance for a fixed θ are λ, γ, N , M , and the array topology [9]. Note that we can also rewrite (2) as…”

Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

“…The analytical formulation of the CRB for DOA estimation for any number of sources, and for any arbitrary array geometry is derived in [8]. In [9], the CRB for a onedimensional linear array is explicitly illustrated. It is also shown in [10], that the array geometry dependent terms in the expression of the CRB are independent of the narrowband/wideband assumption on the signal.…”

Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

“…It is also shown in [10], that the array geometry dependent terms in the expression of the CRB are independent of the narrowband/wideband assumption on the signal. For a deterministic DOA θ, the expression of the CRB denoted by C(θ) (or the Fisher information denoted by F (θ)) for single source DOA estimation is given by [9] …”

Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

“…The parameter ǫ > 0 which is used in the updating procedure is chosen to be very small to sparsifyŵ (k) . We use the MATLAB implementation of CVX [13], for solving the SDP problem (9). …”

Section: Sparsity-enhancing Iterative Algorithmmentioning

confidence: 99%

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Sparsity-enforcing sensor selection for DOA estimation

Roy

Chepuri

Leus

2013

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-The sensitivity of direction-of-arrival (DOA) estimation to different array geometries motivates the design of optimal sensor constellations. We propose a framework for array geometry design for a linear array with fixed aperture and fixed inter-element spacing, where the array geometry design is formulated as a sensor selection problem. The sensor selection is performed such that it achieves a desired Cramér-Rao bound (CRB) for estimating the DOA of a single source. The nonuniformity of the sensor selection typically results in sidelobes. These sidelobes are suppressed in a specified angular sector again via sensor selection. The aforementioned problems are jointly casted as a semidefinite programming (SDP) problem which can be efficiently solved in polynomial time. Simulations exhibit the trade-offs among the number of selected sensors, sidelobe minimization, and CRB of the DOA estimates.

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Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

Section: B Crb For Single Source Doa Estimationmentioning

confidence: 99%

“…The parameter ǫ > 0 which is used in the updating procedure is chosen to be very small to sparsifyŵ (k) . We use the MATLAB implementation of CVX [13], for solving the SDP problem (9). …”

Section: Sparsity-enhancing Iterative Algorithmmentioning

confidence: 99%

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Sparsity-enforcing sensor selection for DOA estimation

Roy

Chepuri

Leus

2013

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…In Fig. 2(b), for comparison purpose, the Cramer-Rao lower bound (CRLB) [44], [45] is also presented. In the next simulation, we present all the CRLB in the RMSE results for the purpose of comparison.…”

Section: T-f Disjoint Sourcesmentioning

confidence: 99%

DOA Estimation of Multiple LFM Sources Using a STFT-based and FBSS-based MUSIC Algorithm

Cui¹,

Chen²,

Huang³

et al. 2017

RADIOENGINEERING

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Abstract. Direction of arrival (DOA) estimation is an important problem in array signal processing. An effective multiple signal classification (MUSIC) method based on the short-time Fourier transform (STFT) and forward/ backward spatial smoothing (FBSS) techniques for the DOA estimation problem of multiple time-frequency (t-f) joint LFM sources is addressed. Previous work in the area e. g. STFT-MUSIC algorithm cannot resolve the t-f completely or largely joint sources because they can only select the single-source t-f points. The proposed method constructs the spatial t-f distributions (STFDs) by selecting the multiple-source t-f points and uses the FBSS techniques to solve the problem of rank loss. In this way, the STFT-FBSS-MUSIC algorithm can resolve the t-f largely joint or

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Sparse Arrays: Fundamentals

Vaidyanathan,

Kulkarni

2023

Sparse Arrays for Radar, Sonar, and Communications

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Temporal and spatial sampling influence on the estimates of superimposed narrowband signals: when less can mean more

Cited by 50 publications

References 29 publications

Sparsity-enforcing sensor selection for DOA estimation

Sparsity-enforcing sensor selection for DOA estimation

DOA Estimation of Multiple LFM Sources Using a STFT-based and FBSS-based MUSIC Algorithm

Sparse Arrays: Fundamentals

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