Joint angle and delay estimation using shift-invariance techniques

Veen, A.‐J. van der; Vanderveen, M.C.; Paulraj, A.

doi:10.1109/78.655425

Cited by 258 publications

(116 citation statements)

References 25 publications

(60 reference statements)

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“…In our context, joint parameter estimation is discussed in a number of papers, including joint azimuth and elevation angle estimation [2], joint frequency and 2-D angle estimation [3], [4], and joint angle and delay estimation [5]. Basically, these methods rely on the fact that each parameter is estimated from a certain eigenvalue problem, where all eigenvalue problems share the same eigenvectors (which are related to the beamforming vectors).…”

Section: Introductionmentioning

confidence: 99%

Analysis of joint angle-frequency estimation using ESPRIT

Lemma

Veen

Deprettere

2003

IEEE Trans. Signal Process.

190

188

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Abstract-High-resolution parameter estimation techniques have recently been applied to jointly estimate multiple signal parameters. In this work, we consider the problem of determining the directions and center frequencies of a number of narrowband sources in a band of interest. We present a joint angle-frequency estimation method, based on the multidimensional ESPRIT algorithm. A perturbation error analysis gives bounds on the parameter estimates and provides optimal values for the temporal and spatial smoothing parameters. The analysis is shown to be consistent with simulation results.

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Section: Introductionmentioning

confidence: 99%

Analysis of joint angle-frequency estimation using ESPRIT

Lemma

Veen

Deprettere

2003

IEEE Trans. Signal Process.

190

188

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“…MATRIX PENCIL USING UNIFORM CIRCULAR ARRAY We consider a circular array of isotropic antennas evenly spaced d x and d y respectively along the axes OX and OY, where we assume dx = dy = d. The network receives signals Ms with the angles of incidence (θ q , φ q ), which are respectively φ q ,θ q and the directions of arrival in elevation and in azimuth. The information on the arrival direction is contained in the eigenvalues of the two transformation matrices that bind respectively subnets 1 and 2 in the X direction and subnets 3 & 4 in the Y-direction [17]- [18]. The values of α x and α y are written in the following form:…”

Section: Matrix Pencil Based On Uniform Linear Arraymentioning

confidence: 99%

Matrix Pencil for Direction Of Arrival Using Smart Antenna

Ihedrane¹,

SeddikBri²

2016

IJET

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Abstract-The main purpose of the smart antenna system is the selection of smart algorithms for adaptive array to estimate the Direction Of Arrival (DOA). By using Matrix Pencil (MP) algorithms the weight of the antenna array can be adjusted to form certain amount of adaptive beam to track corresponding users automatically and to minimize interference arising from others by introducing nulls in their direction. The interference can be suppressed and the desire signal can be extracted. One of the adaptive Matrix Pencil algorithm Least Mean Square Algorithm (LMS) is presented on this article using Uniform Circular Array (UCA) structure. Smart antenna incorporates this algorithm in coded form which calculates complex weights according to the signal environment. The efficiency of (LMS) algorithm is compared on the basis of normalized array factor and Root Mean Square Error (RMSE) for mobile communication. The results demonstrate clearly that the Matrix Pencil investigate in this work is more accurate and stable compared to the published measure.Keyword-Smart Antenna, DOA, LMS, MP, RMSE, UCA I. INTRODUCTION Smart antenna has been widely used in many applications such as radar, sonar and wireless communication systems. Considerable research efforts have been made to estimate the direction of arrival (DOA) and various array signal process techniques for DOA estimation have been proposed. In particular, the DOA estimation for uniform circular arrays (UCAs) has been developed in these scenarios, which desired all-azimuth angle coverage. By the virtue of their geometry, UCAs are able to provide 360°of coverage in azimuth plane. Moreover, they are known to be is isotropic. That is, they can estimate the DOA of incident signal with uniform resolution in the azimuth plane. In addition, direction patterns synthesized with UCAs can be electronically rotated in the plane of the array without significant change of beam shape [1]- [4].There are various methods to estimate the angle of arrival (DOA) of radio signals on the antenna array. DOA estimation techniques can be broadly divided into three different categories namely; conventional methods subspace based methods and maximum likelihood methods. Convolution methods are based on the concepts of beam forming and null steering, but it requires a large number of elements to provide high resolution. Examples of this method are delay and sum and Capon's minimum variance method [2].One major limitation of this method is poor resolution that is its ability to separate closely spaced signals. Unlike conventional methods, subspace methods exploit the information of the received data resulting in high resolution. Two main subspace based algorithms are Multiple Signal Classification and Estimation of Signal Parameters via Rotational Invariance Techniques.The DOA algorithms are classified as quadratic (non subspace) type and subspace type. The Barltett and Capon (Minimum Variance Distortion less Response) [3] are quadratic type algorithms. Both methods are highly dependent on physical size ...

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“…Many of them assume that the spatial signatures are parameterized by the corresponding directions-of-arrival (DOA), as in [24] and [25]. While these methods may exploit the full space-time structure of the signals, they involve the optimization of multidimensional nonlinear cost functions.…”

Section: B Simplified Signal Modelmentioning

confidence: 99%

“…While these methods may exploit the full space-time structure of the signals, they involve the optimization of multidimensional nonlinear cost functions. Only a few cases that resort to particular array configurations allow a closed-form estimation of the delays and DOAs, e.g., [25]. To obtain simpler criteria, most methods presume that the noise is spatially white, which makes them incapable of mitigating directional interferences.…”

Section: B Simplified Signal Modelmentioning

confidence: 99%

“…If the ML criterion is applied to the output signal of the beamformer , the resulting estimates are (24) (25) It is very interesting to observe that the ML criterion proposed in (19) can be expressed as a function of the ML criterion using only temporal information and the cost function based on the MVB: (26) In Sections IV-C and VI-C2, we will dwell on the implications of this expression.…”

Section: B Simplified Signal Modelmentioning

confidence: 99%

See 1 more Smart Citation

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

Seco-Granados

Fernandez–Rubio

Fernández‐Prades

2005

IEEE Trans. Signal Process.

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Abstract-This paper addresses the estimation of the code-phase (pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. The signal is received by an antenna array in a scenario with interference and multipath propagation. These two effects are generally the limiting error sources in most high-precision positioning applications. A new estimator of the code-and carrier-phases is derived by using a simplified signal model and the maximum likelihood (ML) principle. The simplified model consists essentially of gathering all signals, except for the direct one, in a component with unknown spatial correlation. The estimator exploits the knowledge of the direction-of-arrival of the direct signal and is much simpler than other estimators derived under more detailed signal models. Moreover, we present an iterative algorithm, that is adequate for a practical implementation and explores an interesting link between the ML estimator and a hybrid beamformer. The mean squared error and bias of the new estimator are computed for a number of scenarios and compared with those of other methods. The presented estimator and the hybrid beamforming outperform the existing techniques of comparable complexity and attains, in many situations, the Cramér-Rao lower bound of the problem at hand.

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Joint angle and delay estimation using shift-invariance techniques

Cited by 258 publications

References 25 publications

Analysis of joint angle-frequency estimation using ESPRIT

Analysis of joint angle-frequency estimation using ESPRIT

Matrix Pencil for Direction Of Arrival Using Smart Antenna

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

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