Operator approach to performance analysis of root-MUSIC and root-min-norm

Krim, Hamid; Forster, Philippe; Proakis, J.G.

doi:10.1109/78.143441

Cited by 101 publications

(97 citation statements)

References 9 publications

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“…In this section, we compare the performance of the proposed method via simulations with that of other methods, including MUSIC, C-MUSIC, and Minimum Norm (MN) [23]. We consider a ULA with M = 13 sensors and K = 2 independent narrowband sources.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Low Complexity Direction of Arrival Estimation Algorithm by Reinvestigating the Sparse Structure of Uniform Linear Arrays

Sun¹,

Lan²,

Gao³

et al. 2016

PIER C

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Abstract-In this paper, we present a new computationally efficient method for direction-of-arrival (DOA) estimation in uniform linear arrays (ULAs). A sparse uniform linear array (SULA) structure is firstly extracted from the conventional ULA to exploit its advantage in high resolution. By performing the multiple signal classification (MUSIC), the noise subspace of the SULA is simultaneously orthogonal to the steering vectors corresponding to the true DOAs and several virtual DOAs, where all the true and virtual DOAs for each source are uniformly distributed in the sine domain. Then we divide the total angular field into several small sectors and search over an arbitrary sector. Finally, the true DOAs can be distinguished by the noise subspace of the original ULA. Since the proposed method involves a limited spectral search and a reduced-dimension noise subspace, hence it is quite computationally efficient. Simulation results are provided to verify the effectiveness of the proposed method in terms of computational complexity, estimation accuracy, and resolution performance.

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Section: Simulation Resultsmentioning

confidence: 99%

A Low Complexity Direction of Arrival Estimation Algorithm by Reinvestigating the Sparse Structure of Uniform Linear Arrays

Sun¹,

Lan²,

Gao³

et al. 2016

PIER C

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“…Using the standard perturbation result for orthogonal projectors [30] (see also [29]) applied to Πỹ associated with the noise subspace of Rỹ δ(Πỹ) = −Πỹδ(Rỹ)S † − S † δ(Rỹ)Πỹ + o (δ(Rỹ)) (16) with S def =H(h)R xH (h) H , the asymptotic behaviors of vec(Πỹ ,T ) and Rỹ ,T are directly related. The standard theorem (see e.g., [28, p. 122 …”

Section: Appendixmentioning

confidence: 99%

Performance Limits of Alphabet Diversities for FIR SISO Channel Identification

Delmas

Meurisse

Comon

2009

IEEE Trans. Signal Process.

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“…This estimation error induces an error on the estimatesΠ c andΠ ⊥ c . It is shown in [32] that the projector estimates are given up to the second order with respect to ∆Σ by:…”

Section: Proofmentioning

confidence: 99%

“…Since all considered estimators have been shown to be consistent, the SINR loss is evaluated for large K by means of a perturbation analysis [32]. Starting from the perturbations onR ,Π c andΠ ⊥ c , the SINR loss ρ of Eq.…”

Section: Proofmentioning

confidence: 99%

Exploiting persymmetry for low-rank Space Time Adaptive Processing

et al. 2014

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Reducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller Signal Interference plus Noise Ratio (SINR) loss than classical STAP filters, only 2r secondary data (where r is the clutter rank) instead of 2m (where m is the data size) are required to reach the classical 3dB SNR loss. By using other features of the radar system, other properties of the CM can be exploited to further reduce the number of secondary data: this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval, which results in a persymmetric structure of the noise CM. In this context, we propose to combine this property of the CM and the LR structure of the clutter to perform CM estimation. In this paper, the resulting STAP filter is shown, both theoretically and experimentally, to exhibit good performance with fewer secondary data: 3dB SINR loss is achieved with only r secondary data.

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Operator approach to performance analysis of root-MUSIC and root-min-norm

Cited by 101 publications

References 9 publications

A Low Complexity Direction of Arrival Estimation Algorithm by Reinvestigating the Sparse Structure of Uniform Linear Arrays

A Low Complexity Direction of Arrival Estimation Algorithm by Reinvestigating the Sparse Structure of Uniform Linear Arrays

Performance Limits of Alphabet Diversities for FIR SISO Channel Identification

Exploiting persymmetry for low-rank Space Time Adaptive Processing

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