Computation of spectral and root MUSIC through real polynomial rooting

An algorithm for joint direction-of-departures (DODs) and direction-of-arrivals (DOAs) estimation using real polynomial rooting in bistatic multiple-input multiple-output (MIMO) radar is presented. Compared with the conventional spectral Capon method, the proposed approach estimates the angles by the alternating maximization (AM) algorithm and onedimensional real polynomial rooting instead of two-dimensional angle search to reduce the computational burden. The novel real root-Capon method can perform well even without the prior knowledge of the initial values of unknown parameters which is a prerequisite to AM algorithm. Furthermore, the DODs and DOAs of targets can be paired automatically. Numerical examples validate the effectiveness of the algorithm.

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“…The conformal transformation which maps the unit circle into the real line is introduced [8] z j u j z j…”

Section: Novel Root-capon Methodsmentioning

confidence: 99%

Joint DOD and DOA Estimation Using Real Polynomial Rooting in Bistatic MIMO Radar

Liu

Liao

2010

2010 International Conference on Multimedia Technology

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“…For notational simplicity, (8) can be rewritten as (9) where (10) (11) (12) and (13) In practice, (10) and (11) can be respectively reformulated in summation form, (15) where is the sum of the elements along the diagonal line of , i.e., (16) and is the sum of the elements along the diagonal line of , i.e.,…”

Section: Signal Model and ML Angle Estimationmentioning

confidence: 99%

“…Compared to the conventional fine grid search method, the presented method can obtain more accurate solution of the ML estimator with lower complexity. The presented method is motivated by the reference [12], which implements spectral MUSIC by real polynomial rooting. Relative to [12], the presented method extends the application of real polynomial rooting from the quadratic form case to the general Rayleigh quotient case, and gives fast computation on polynomial coefficients utilizing the properties owned by the transformation matrix, which is not investigated in [12].…”

Section: Introductionmentioning

confidence: 99%

Fast Realization of Maximum Likelihood Angle Estimation With Small Adaptive Uniform Linear Array

Wang

Bao

2010

IEEE Trans. Antennas Propagat.

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A maximum likelihood (ML) angle estimator can obtain optimal angle estimation performance. A fast implementation of ML angle estimation is developed with small adaptive uniform linear arrays (ULA). The presented method obtains the extreme points of the ML function employing real polynomial rooting, and then determines the ML solution by searching for the maximum among the local maximum values of the ML function. It can obtain the global optimal solution of the ML estimator with low computational complexity.

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“…So the real-valued eigendecomposition has a reduced computational complexity than the complex one, approximately by a factor of four [3] .…”

Section: Q R Qmentioning

confidence: 99%

A new fast 2-D superresolution imaging algorithm

Liang¹,

Rui-guang

Zhu

et al. 2009

2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar

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A novel method is proposed for reconstructing the 2-D scattering centres of radar target, research the frequency scatter model of electromagnetic scattering, we resolve the problem by extracting two 1-D scattering centres parameter instead of extracting directly 2-D scattering centres parameter, separately estimate the 1-D scattering centres in range dimension and cross dimension using conjugate unitary Root-MUSIC algorithm, group them and attain all possible spatial frequency pairs, then the unveracious frequency pairs are eliminated by spatial filtering, achieve the 2-D scattering centres parameter.

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Computation of spectral and root MUSIC through real polynomial rooting

Cited by 39 publications

References 4 publications

Joint DOD and DOA Estimation Using Real Polynomial Rooting in Bistatic MIMO Radar

Joint DOD and DOA Estimation Using Real Polynomial Rooting in Bistatic MIMO Radar

Fast Realization of Maximum Likelihood Angle Estimation With Small Adaptive Uniform Linear Array

A new fast 2-D superresolution imaging algorithm

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