Multidimensional Frequency Estimation With Finite Snapshots in the Presence of Identical Frequencies

Li, Jun; Liu, Xiangqian; Ma, Xiaoli

doi:10.1109/tsp.2007.899530

Cited by 36 publications

(81 citation statements)

References 24 publications

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“…Perturbation analysis of IMDF have been done in [6]. However, the obtained expressions require the calculation of the SVD of the MH matrix H. To get simplified perturbation expressions we use the following fact: from (9), K IMDF can be written as a linear combination of…”

Section: Imdf Perturbationsmentioning

confidence: 99%

“…They include linear prediction-based methods such as 2-D TLS-Prony [10], and subspace approaches such as matrix enhancement and matrix pencil (MEMP) [3], 2-D ESPRIT [8], improved multidimensional folding (IMDF) [7,6], Tensor-ESPRIT [2], principal-singular-vector utilization for modal analysis (PUMA) [14,13]. Among the most promising are N-D ESPRIT [8,12] and IMDF [7,6]. Both methods use the eigenvalue decomposition (EVD) of a so-called shift matrix constructed from the estimated basis of the signal subspace, but the modes are extracted differently: from eigenvalues in ND-ESPRIT and from eigenvectors in IMDF.…”

Section: Introductionmentioning

confidence: 99%

“…-We derive simple expressions of first-order perturbations of IMDF that do not need to calculate the SVD of the MH matrix as it is needed in expressions given in [6].…”

Section: Introductionmentioning

confidence: 99%

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High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?

Usevich

Sahnoun

Comon

2017

Latent Variable Analysis and Signal Separation

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Abstract. In subspace-based methods for mulditimensional harmonic retrieval, the modes can be estimated either from eigenvalues or eigenvectors. The purpose of this study is to find out which way is the best. We compare the state-of-the art methods N-D ESPRIT and IMDF, propose a modification of IMDF based on least-squares criterion, and derive expressions of the first-order perturbations for these methods. The theoretical expressions are confirmed by the computer experiments.

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Section: Imdf Perturbationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?

Usevich

Sahnoun

Comon

2017

Latent Variable Analysis and Signal Separation

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“…We summarize our main result on statistical identifiability for the proposed algorithm in the following theorem [46]. (13), in the absence of noise, the parameter set ({ω f,n } N n=1 , {c f (t)} T t=1 ), f = 1, .…”

Section: B61 the Eigenvector-based Algorithm For N -D Frequency Estmentioning

confidence: 99%

“…We have derived the theoretic error variance of the eigenvector-based algorithm in [46]. We have also derived the Cramér-Rao Bound for multidimensional frequency estimation models [46].…”

Section: B62 Optimization Of the Eigenvector-based Algorithmmentioning

confidence: 99%

Co-channel Interference Mitigation for Robust Coexistence of Frequency Hopped Networks

Liu¹

2006

Self Cite

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Public Reporting Burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comment regarding this burden estimate or any other aspect of this collection of information, including suggesstions for reducing this burden, ABSTRACTTo meet the military's increased need for rapidly deployable communication solutions, wireless networks are becoming increasingly common within tactical environments. Frequency hopping (FH) is widely used for radio transmission in such networks due to its low probability of detection/interception. With the increasing deployment, multiple networks occupying overlapping frequency bands are likely to coexist in a physical environment, especially in tactical operations, emergency situations, or dense-populated areas. Consequently co-channel interference due to frequency collisions can become a major performance limiting factor. In this project, we developed a novel interference mitigation technique based on multidimensional frequency estimation coupled with the expectation-maximization principle, which effectively resolves collisions among non-collaborative networks, thus enabling robust coexistence of multiple FH networks. To deal with possible receiver-transmitter mismatch, we also designed a low-complexity model order variation detection method. Novel multidimensional frequency estimation algorithms are also investigated. J. Li and X. Liu, "A software demonstration: collision resolution for robust coexistence of multiple wireless

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Multidimensional Harmonic Retrieval: Exact, Asymptotic, and Modified Cramé‐RéRao Bounds

2011

Digital Spectral Analysis

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Multidimensional Frequency Estimation With Finite Snapshots in the Presence of Identical Frequencies

Cited by 36 publications

References 24 publications

High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?

High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?

Co-channel Interference Mitigation for Robust Coexistence of Frequency Hopped Networks

Multidimensional Harmonic Retrieval: Exact, Asymptotic, and Modified Cramé‐RéRao Bounds

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