| 1. |
- Andersson, Sören
(author)
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Optimal Dimension Reduction for Sensor Array Signal Processing
- 1991
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In: Proceedings of the 25h Asilomar Conference on Signals, Systems and Computers. - Linköping : Linköping University. - 0818624701 ; , s. 918-922
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Reports (other academic/artistic)abstract
- The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full-dimension data into a lower-dimensional space. In the present work, a transformation matrix is derived, that makes it possible to attain the full-dimension Cramer-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the lower-dimension estimates of the source localizations as that achievable by using the full-dimension data. Furthermore, a method is devised for designing the transformation matrix.
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| 2. |
- Ottersten, Björn, et al.
(author)
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Local Modeling and Robust Estimation for High-Resolution Direction Finding
- 1991
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In: Proceedings of the 25th Asilomar Conference on Signals Systems and Computers. - Linköping : Linköping University. - 0818624701 ; , s. 1005-1009 vol.2
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Conference paper (peer-reviewed)abstract
- Many signal processing applications are concerned with estimating signal parameters with high accuracy from sensor array measurements. To obtain accurate location estimates, knowledge of the array response is required. Most estimation techniques are sensitive to perturbations in the array model and array calibration is often necessary. The authors discuss the issue of generating the array response parameterization from a finite number of noise-contaminated calibration vectors. An estimation technique based on local parametric modeling of the array response is proposed. The potential improvement using the suggested scheme rather than an ideal array model is demonstrated on real data collected from a full-scale hydroacoustic array. It is shown that, by applying the proposed technique together with a standard direction-of-arrival estimation procedure, a significant reduction in estimation bias can be obtained.
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| 3. |
- Viberg, Mats, et al.
(author)
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Estimation Accuracy of Maximum Likelihood Direction Finding using Large Arrays
- 1991
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In: Proceedings of the 25th Asilomar Conference on Signals, Systems and Computers. - Linköping : Linköping University. - 0818624701 ; , s. 928-932 vol.2
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Conference paper (peer-reviewed)abstract
- The authors analyze the performance of methods for estimating the parameters of narrowband signals arriving at an array of sensors. The deterministic and stochastic maximum likelihood (ML) methods are considered. A performance analysis is carried out for a finite number of snapshots but assuming that the array is composed of a sufficiently large number, m, of sensors. Strong consistency of the parameter estimates is proved and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large (time) 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 Cramer-Rao bound. For many array geometries of practical interest, the array propagation vectors become orthogonal as m as increased. It is shown that the traditional beamforming method provides consistent (but not necessarily efficient) estimates under the assumption. This is true also in the presence of perfectly correlated emitters.
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