Iterative Adaptive Approaches to MIMO Radar Imaging

Roberts, William J.; Stoica, Petre; Li, Jian; Yardibi, Tarik; Sadjadi, Firooz A.

doi:10.1109/jstsp.2009.2038964

Cited by 236 publications

(119 citation statements)

References 30 publications

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“…Further computational savings can be achieved, without more than a marginal sacrificing of performance, by using approximate estimates in place of R N 1 N 2 , defined by (9), (12), and (15), and R N 1 N 2 and P N 1 N 2 , defined by (19) and (20). Reminiscent of the results recently introduced in [15], where a fast approximative CG-based 1-D IAA algorithm was presented, and later extended in [34] to a block-recursive (1-D) formulation applied to blood velocity estimation in ultrasound imaging, we here propose approximative estimates for the covariance matrices involved in all the SMLAs.…”

Section: N1n2mentioning

confidence: 99%

“…1 In order to estimate the noise variance, we use IAA-R (a regularized IAA algorithm which accounts for the additive noise [12]). 2 In the examined examples, no significant further improvement was achieved after the specified number of iterations.…”

Section: Spectral Estimationmentioning

confidence: 99%

“…To alleviate this problem, recent work has examined various forms of sparse estimation techniques, such as the sparse learning via iterative minimization (SLIM) method [6], the iterative adaptive approach (IAA) [7], and more recently a set of iterative sparse maximum likelihood-based approaches (SMLA) [8,9]. This class of methods have been found to offer significant performance improvements as compared to the traditional methods not exploiting the sparsity of the signal, generally providing reliable high-resolution estimates with excellent side lobe suppression [10][11][12][13][14]. Yet all these approaches suffer from being computationally cumbersome, which has resulted in a series of recent works focusing on formulating computationally efficient implementations for the SLIM and IAA estimates [15][16][17][18]14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAR imaging via efficient implementations of sparse ML approaches

Glentis¹,

Zhao²,

Jakobsson³

et al. 2014

Signal Processing

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a b s t r a c tHigh-resolution spectral estimation techniques are of notable interest for synthetic aperture radar (SAR) imaging. Several sparse estimation techniques have been shown to provide significant performance gains as compared to conventional approaches. We consider efficient implementation of the recent iterative sparse maximum likelihood-based approaches (SMLAs). Furthermore, we present approximative fast SMLA formulation using the Quasi-Newton approach, as well as consider hybrid SMLA-MAP algorithms. The effectiveness of the discussed techniques is illustrated using numerical and experimental examples.

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Section: N1n2mentioning

confidence: 99%

Section: Spectral Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SAR imaging via efficient implementations of sparse ML approaches

Glentis¹,

Zhao²,

Jakobsson³

et al. 2014

Signal Processing

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show abstract

“…IAA의 경우, 국소 수렴(local convergence)한다는 것이 알려져 있으나, 전역 수렴(global convergence)성 질에 관해서는 알려져 있지 않다 [10] . IAA 알고리듬은 표 1과 같이 정리된다.…”

Section: -2 Iaaunclassified

Spectral Analysis Method to Eliminate Spurious in FMICW HRR Millimeter-Wave Seeker

양희성¹,

전주환²,

송성찬³

2012

The Journal of Korean Institute of Electromagnetic Engineering

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In this thesis, we develop a spectral analysis scheme to eliminate the spurious peaks generated in HRR Millimeterwave Seeker based on FMICW system. In contrast to FMCW system, FMICW system generates spurious peaks in the spectrum of its IF signal, caused by the periodic discontinuity of the signal. These peaks make the accuracy of the system depend on the previously estimated range if a band pass filter is utilized to eliminate them and noise floor go to high level if random interrupted sequence is utilized and in case of using staggering process, we must transmit several waveforms to obtain overlapped information. Using the spectral analysis one of the schemes such as IAA(Iterative Adaptive Approach) and SPICE(SemiParametric Iterative Covariance-based Estimation method) which were introduced recently, the spurious peaks can be eliminated effectively. In order to utilize IAA and SPICE, since we must distinguish between reliable data and unreliable data and only use reliable data, STFT(Short Time Fourier Transform) is applied to the distinguishment process.

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“…The sparse learning via iterative minimization (SLIM) method was introduced in the context of MIMO radar imaging in [7], and can be viewed as a version of the well-known (regularized) FOCUSS algorithm [8], although including also the iterative estimation of the noise variance (see also [9]). Both the IAA and SLIM algorithms have been shown to converge locally [7,10], as well as to yield excellent performance for both complete or incomplete data sets. Regrettably, both algorithms are also computationally cumbersome, and several works have focused on forming various computationally efficient implementations for uniformly and non-uniformly sampled data sequences [11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient sparsity-inducing coherence spectrum estimation of complete and non-complete data sets

2013

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The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further the recent development of exploiting the estimators' inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these formulations to the coherence estimation using IAA and SLIM formulations. The performance of the proposed algorithms and implementations are illustrated both with theoretical complexity measures and with numerical simulations.

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Iterative Adaptive Approaches to MIMO Radar Imaging

Cited by 236 publications

References 30 publications

SAR imaging via efficient implementations of sparse ML approaches

SAR imaging via efficient implementations of sparse ML approaches

Spectral Analysis Method to Eliminate Spurious in FMICW HRR Millimeter-Wave Seeker

Computationally efficient sparsity-inducing coherence spectrum estimation of complete and non-complete data sets

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