| 1. |
- Andersson, Sören, et al.
(författare)
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A Study of Adaptive Arrays for Mobile Communication Systems
- 1990
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.
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| 2. |
- Viberg, Mats, et al.
(författare)
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Performance of Subspace based State-Space System Identification Methods
- 1993
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Ingår i: Proceedings of the 12th IFAC World Congress. ; , s. 369-
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Traditional prediction-error techniques for multivariable system identification require canonical descriptions using a large number of parameters. This problem can be avoided using subspace based methods, since these estimate a state-space model directly from the data. The main computations consist of a QR-decomposition and a singular-value decomposition. Herein, a subspace based technique for identifying general finite-dimensional linear systems is presented and analyzed. The technique applies to general noise covariance structures. Explicit formulas for the asymptotic pole estimation error variances are given. The proposed method is found to perform comparable to a prediction error method in a simple example.
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| 3. |
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| 4. |
- Ottersten, Björn, et al.
(författare)
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Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
- 1991
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Sensor array signal processing deals with the problem of extracting information from a collection of measurements obtained from sensors distributed in space. The number of signals present is assumed to be finite, and each signal is parameterized by a finite number of parameters. Based on measurements of the array output, the objective is to estimate the signals and their parameters. This research area has attracted considerable interest for several years. A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array.
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| 5. |
- Stoica, Petre, et al.
(författare)
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Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields
- 1991
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposed in the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noise covariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment.
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| 6. |
- Swindlehurst, A. Lee, et al.
(författare)
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Subspace Fitting with Diversely Polarized Antenna Arrays
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Diversely polarized antenna arrays are widely used in RF applications. The diversity of response provided by diversely polarized antenna arrays can greatly improve direction-finding performance over arrays sensitive to only one polarization component. For d emitters, directly implementing a multidimensional estimation algorithm would require a search for 3d parameters: d directions of arrival (DOAs), and 2d polarization parameters. A more efficient solution is presented based on the noise subspace fitting (NSF) algorithm. It is shown how to decouple the NSF search into a two-step procedure, where the DOAs are estimated separately. The polarization parameters are then obtained by solving a linear system of equations. The advantage of this approach is that the search dimension is reduced by a factor of three, and no initial polarization estimate is required. The algorithm can be shown to yield asymptotically minimum variance estimates: provided no perfectly coherent signals are present. Simulation examples are included.
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| 7. |
- Viberg, Mats, et al.
(författare)
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Array Processing in Correlated Noise Fields Using Instrumental Variables and Subspace Fitting
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region.
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| 8. |
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| 9. |
- Viberg, Mats, et al.
(författare)
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Performance Analysis of Direction Finding with Large Arrays and Finite Data
- 1992
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper considers analysis of methods for estimating the parameters of narrow-band signals arriving at an array of sensors. This problem has important applications in, for instance, radar direction finding and underwater source localization. The so-called deterministic and stochastic maximum likelihood (ML) methods are the main focus of this paper. A performance analysis is carried out assuming a finite number of samples and that the array is composed of a sufficiently large number of sensors. Several thousands of antennas are not uncommon in, e.g., radar applications. Strong consistency of the parameter estimates is proved, and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large sample case, the present analysis shows that the accuracy is the same for the two ML methods. The covariance matrix of the estimation error attains the Cramér-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for sufficiently large m. Surprisingly, this is shown to be possible also in the presence of perfectly correlated emitters.
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| 10. |
- Viberg, Mats
(författare)
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Sensitivity of Parametric Direction Finding to Colored Noise Fields and Undermodeling
- 1992
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A fundamental assumption for most direction finding algorithms is that the spatial correlation structure of the background noise (i.e., the correlation from sensor to sensor) is known to within a multiplicative scalar. In practive, this is often achieved by measuring the array covariance when no signals are present, a procedure which is unavoidably subjected to errors. The presence of undetected weak signals gives rise to similar perturbations. In this paper, the effect of such modeling errors on parametric estimation techniques is examined. First-order expressions for the mean square error (MSE) of the parameter estimates are derived for the deterministic and stochastic maximum likelihood methods and the weighted subspace fitting technique. The spatial noise correlation structures that lead to maximum performance loss are identified under different assumptions. In case of high signal-to-noise ratio, it is found that the MSE can be increased by a factor equal to the number of sensors in the array, as compared to spatially white noise. Furthermore, it is demonstrated that the presence of a relatively weak (− 15 dB) undetected signal can result in a large bias (≈1°) on the estimates of the other signal directions.
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