| 1. |
- Göransson, Bo, et al.
(författare)
-
Spatial and Temporal Frequency Estimation of Uncorrelated Signals Using Subspace Fitting
- 1996
-
Ingår i: IEEE Signal Processing Workshop on Statistical Signal and Array Processing. ; , s. 94-96
-
Konferensbidrag (refereegranskat)abstract
- We present a novel method for spatial and temporal frequency estimation in the case of uncorrelated sources. By imposing the diagonal structure given in the signal covariance matrix, it is possible to improve the performance of subspace based estimators. The proposed method combines ideas from subspace and covariance matching methods to yield a non-iterative frequency estimation algorithm. In a numerical example we show that the estimator has a lower small sample resolution threshold than root-MUSIC and similar large sample performance.
|
|
| 2. |
- Hyberg, Per, et al.
(författare)
-
Array interpolation and DOA MSE reduction
- 2005
-
Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 53:12, s. 4464-4471
-
Tidskriftsartikel (refereegranskat)abstract
- Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper [21], a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE,improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper.
|
|
| 3. |
|
|
| 4. |
- Hyberg, Per, et al.
(författare)
-
Array Mapping : Optimal Transformation Matrix Design
- 2002
-
Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. - : IEEE. ; , s. 2905-2908
-
Konferensbidrag (refereegranskat)abstract
- Mapping of the data output vector from an existing antenna array onto the data vector of an imaginary array of more suitable configuration is well known in array signal processing. By mapping onto an array manifold of lower dimension or uniform structure for example., processing speed can be improved. Conditions for the retaining of DOA error variance under such mapping have been formulated by several authors but the equally important systematic mapping errors, the bias, has been less treated to date. This paper uses a geometrical interpretation of a Taylor expansion of the DOA estimator cost function to derive an alternative design of the mapping matrix that almost completely removes the bias. The key feature of the proposed design is that it takes the orthogonality between the manifold mapping errors and certain gradients of the estimator cost function into account.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Jansson, Magnus, et al.
(författare)
-
A Subspace Method for Direction of Arrival Estimation of Uncorrelated Emitter Signals
- 1999
-
Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X. ; 47:4, s. 945-956
-
Tidskriftsartikel (refereegranskat)abstract
- A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results.
|
|
| 8. |
- Jansson, Magnus, et al.
(författare)
-
Analysis of a Subspace-based Spatial Frequency Estimator
- 1997
-
Ingår i: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-97). ; , s. 4001-4004
-
Konferensbidrag (refereegranskat)abstract
- In a previous paper we presented a novel method for spatial and temporal frequency estimation assuming that the sources are uncorrelated. The current paper analyzes this method in the case of spatial frequency estimation. In particular an optimal weighting matrix is derived and it is shown that the asymptotic variance of the frequency estimates coincides with the relevant Cramer-Rao lower bound. This means that the estimator is in large samples an efficient subspace-based spatial frequency estimator. The proposed method thus utilizes the a priori knowledge about the signal correlation as opposed to previously known subspace estimators. Moreover, when a uniform linear array is employed, it is possible to implement the estimator in a non-iterative fashion.
|
|
| 9. |
|
|
| 10. |
- Jansson, Magnus, et al.
(författare)
-
Ml estimation of covariance matrices with kronecker and persymmetric structure
- 2009
-
Ingår i: 2009 IEEE 13TH DIGITAL SIGNAL PROCESSING WORKSHOP & 5TH IEEE PROCESSING EDUCATION WORKSHOP, VOLS 1 AND 2, PROCEEDINGS. - NEW YORK : IEEE. - 9781424436767 ; , s. 298-301
-
Konferensbidrag (refereegranskat)abstract
- Estimation of covariance matrices is often an integral part in many signal processing algorithms. In some applications, the covariance matrices can be assumed to have certain structure. Imposing this structure in the estimation typically leads to improved accuracy and robustness (e.g., to small sample effects). In MIMO communications or in signal modelling of EEG data the full covariance matrix can sometimes be modelled as the Kronecker product of two smaller covariance matrices. These smaller matrices may also be structured, e.g., being Toeplitz or at least persymmetric. In this paper we discuss a recently proposed closed form maximum likelihood (ML) based method for the estimation of the Kronecker factor matrices. We also extend the previously presented method to be able to impose the persymmetric constraint into the estimator. Numerical examples show that the mean square errors of the new estimator attains the Cramer-Rao bound even for very small sample sizes.
|
|
