Theoretical performance prediction of the MUSIC algorithm

Farrier, D.R.; Jeffries, D.J.; Mardani, R.

doi:10.1049/ip-f-1.1988.0029

Cited by 19 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the principle of the element‐space multiple signal classification (MUSIC) estimator [31–33], we obtain the transmit beamspace‐based MUSIC cost function [34, 35]

f false(r, θ false) = \frac{a_{s}^{H} false(r, θ, t false) a_{s} false(r, θ, t false)}{a_{s}^{H} false(r, θ, t false) P_{n} a_{s} false(r, θ, t false)}

where P n is the projection matrix onto the noise subspace

P_{n} = E_{n} E_{n}^{H} .

The range and angle of targets can be estimated from the L magnitude peaks of (37)

(}{, {\overset{false^}{r}}_{0}, \overset{false^}{θ}) = \arg \max_{r, θ} {|f (r, θ)|}^{2} .

Now, the targets can then be localised in range–angle dimension by combining (18) with (39).…”

Section: Difference Co‐array Processing‐based Target Range and Anglmentioning

confidence: 99%

Nested array receiver with time‐delayers for joint target range and angle estimation

Wang

Zhu

2016

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…According to the principle of the element‐space multiple signal classification (MUSIC) estimator [31–33], we obtain the transmit beamspace‐based MUSIC cost function [34, 35]

f false(r, θ false) = \frac{a_{s}^{H} false(r, θ, t false) a_{s} false(r, θ, t false)}{a_{s}^{H} false(r, θ, t false) P_{n} a_{s} false(r, θ, t false)}

where P n is the projection matrix onto the noise subspace

P_{n} = E_{n} E_{n}^{H} .

The range and angle of targets can be estimated from the L magnitude peaks of (37)

(}{, {\overset{false^}{r}}_{0}, \overset{false^}{θ}) = \arg \max_{r, θ} {|f (r, θ)|}^{2} .

Now, the targets can then be localised in range–angle dimension by combining (18) with (39).…”

Section: Difference Co‐array Processing‐based Target Range and Anglmentioning

confidence: 99%

Nested array receiver with time‐delayers for joint target range and angle estimation

Wang

Zhu

2016

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…Regarding the statistical properties of MUSIC, the effects of order estimation errors, that is, the effect of choosing an erroneous G in (13), on the parameter estimates obtained using MUSIC have been studied in [29] in a slightly different context and it was concluded that the MUSIC estimator is more sensitive to underestimation of L than overestimation. The more common case of L being known has been treated in great detail, with the statistical properties of MUSIC having been studied in [30][31][32][33][34].…”

Section: Fundamentalsmentioning

confidence: 99%

“…In [40], θ was shown to be an angle in the usual Euclidean sense. Equations (33), (34), and (35) are not very convenient measures for our purpose since they cannot be calculated from the individual columns of A but rather depend on all of them. This means that optimization of any of these measures would require multidimensional nonlinear optimization over the frequencies {ω l }.…”

Section: Relation To Othermentioning

confidence: 99%

Sinusoidal Order Estimation Using Angles between Subspaces

Christensen

Jakobsson

Jensen

2009

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.

show abstract

“…Considerable research has been devoted to these techniques in recent years because of their superior ability to resolve closely spaced frequencies of multiple, superimposed, sinusoids in noisy signals [11,12,[14][15][16][17][18][19]. We describe the principle of frequency estimation using eigen-decomposition techniques, restricting the discussion to the two important algorithms: MUSIC and its variant, Root-MUSIC.…”

Section: Eigen-decomposition Techniquesmentioning

confidence: 99%

Eigen-decomposition techniques for Loran-C skywave estimation

Bian

1997

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

Eigen-decomposition spectral analysis techniques are used to estimate the delays of Loran-C skywaves. Their performance is evaluated and compared with that of Fourier-based techniques. Results using off-air data are presented. This work establishes the basis on which to design a Loran-C receiver capable of adjusting its sampling point adaptively to the optimal value in a constantly changing skywave environment. Such receivers promise to improve significantly the accuracy and reliability of positioning under adverse operational conditions.

show abstract

Theoretical performance prediction of the MUSIC algorithm

Cited by 19 publications

References 2 publications

Nested array receiver with time‐delayers for joint target range and angle estimation

Nested array receiver with time‐delayers for joint target range and angle estimation

Sinusoidal Order Estimation Using Angles between Subspaces

Eigen-decomposition techniques for Loran-C skywave estimation

Contact Info

Product

Resources

About