| 1. |
|
|
| 2. |
- Gorokhov, Alexei, et al.
(författare)
-
Robust Blind Second-Order Deconvolution
- 1999
-
Ingår i: IEEE Signal Processing Letters. - : IEEE Signal Processing Society. - 1070-9908 .- 1558-2361. ; 6:1, s. 13-16
-
Tidskriftsartikel (refereegranskat)abstract
- Second-order blind deconvolution of single input multiple output (SIMO) finite impulse response (FIR) channels is considered. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of the model order. In this contribution, a robust method using only the second-order statistics is described. It provides high and robust estimation accuracy even for a limited sample size and an unknown model order. In contrast to other suggested approaches, that often exploit properties valid only in the large sample case, the proposed method is applicable both in the large sample and high signal-to-noise ratio (SNR) scenario.
|
|
| 3. |
- Gorokhov, Alexei, et al.
(författare)
-
Robust Blind Second Order Deconvolution of Multiple FIR Channels
- 1998
-
Ingår i: Proceedings IEEE Global Telecommunications Conference, 1998. GLOBECOM 98. The Bridge to Global Integration.. - : IEEE. ; , s. 2062-2067
-
Konferensbidrag (refereegranskat)abstract
- Second order blind deconvolution of single input multiple output (SIMO) FIR channels is considered herein. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of model order. In this article, a robust method using only the second order statistics is described. It provides high estimation accuracy even for a limited sample size and an unknown model order. In contrast to other suggested approaches, that often exploit properties valid only in the large sample case, the proposed method is applicable both in the large sample and high signal-to-noise ratio (SNR) scenario; it also enjoys a simple implementation.
|
|
| 4. |
- Gorokhov, A., et al.
(författare)
-
Some Results on Blind Deconvolution Applied to Digital Communication Signals
- 1997
-
Ingår i: Proceedings of DSP 97. ; , s. 107-110
-
Konferensbidrag (refereegranskat)abstract
- Blind deconvolution techniques applied to spatially and/or temporally oversampled signals have recentlyattracted much interest in the research community. This contribution contains an analysis of experimental data collected from an antenna array in a suburban environment. The Noise Subspace (NS) technique of [1] and the Linear Prediction(LP) method of [2, 4] are examined. The real data examples presented demonstrate a case where joint spatial and temporal deconvolution has clear benefits as compared to decoupled processing.
|
|
| 5. |
- Kristensson, Martin, et al.
(författare)
-
A statistical approach to subspace based blind identification
- 1998
-
Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 46:6, s. 1612-1623
-
Tidskriftsartikel (refereegranskat)abstract
- Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches.
|
|
| 6. |
- Kristensson, Martin, et al.
(författare)
-
Asymptotic Comparison of two Blind Channel Identification Algorithms
- 1997
-
Ingår i: Proc. of IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications. SPAWC 97. - : IEEE. ; , s. 361-364
-
Konferensbidrag (refereegranskat)abstract
- In this paper the performance of two second order based blind channel identification techniques is studied. The methods are compared theoretically and the formulas validated practically by simulations. The first method is a well known subspace approach and the second is a covariance matching estimator. This last estimator should attain the lower bound for the asymptotical estimation error covariance of any second order based blindidentification algorithm.
|
|
| 7. |
- Kristensson, Martin, et al.
(författare)
-
Blind Subspace Identification of a BPSK Communication Channel
- 1996
-
Ingår i: 30:th Asilomar Conference on Signals, Systems & Computers. - : IEEE. ; , s. 828-832
-
Konferensbidrag (refereegranskat)abstract
- This paper considers the problem of blind estimation of multiple FIR channels. When a subspace algorithm is applied to the blind identification problem, incorporating information about the symbol constellation is in general not possible. However, by exploiting special properties of one dimensional symbol constellations (BPSK), it is shown that it is possible to improve or simplify a class of algorithms for blind channel identification. It is also shown that in the case of one dimensional symbol constellations there is a third way, apart from multiple antennas and oversampling, of arriving at a multichannel representation of the communication system.
|
|
| 8. |
- KRISTENSSON, Martin, et al.
(författare)
-
Co-channel interference rejection in a digital receiver
- 2000
-
Patent (populärvet., debatt m.m.)abstract
- The present invention relates to a method for a digital receiver and a receiver exploiting second order statistics for adaptive co-channel interference rejection in wireless communication. It uses digitally I, in phase, and Q, quadrature, branches of a received transmitted signal as input to the receiver, a coarse synchronization and a coarse frequency offset compensation have being performed on the signal. It comprises a means for derotation, means for separation, means for filtering, means for estimating and means for detecting transmitted symbols in the received signal. The invention thereby improving co-channel rejection in wireless communication, thus making it possible to increase the number of communication channels for frequencies used.
|
|
| 9. |
- Kristensson, Martin, et al.
(författare)
-
Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
- 2001
-
Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 49:12, s. 2962-2974
-
Tidskriftsartikel (refereegranskat)abstract
- Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established
|
|
| 10. |
- Kristensson, Martin, et al.
(författare)
-
Further Results on Optimally Weighted Subspace Based Blind Channel Estimation
- 1998
-
Ingår i: Proceedings of the 32th Asilomar Conference on Signals, Systems and Computers. - : IEEE. ; , s. 1579-1583
-
Konferensbidrag (refereegranskat)abstract
- Subspace based identification of model parameters requires a low rank data model. To obtain such a model it is common to collect several snapshots of the observed vector valued sequence in a larger vector. In many cases, this “window” procedure results in a new vector valued process which can be viewed as originating from a low rank data model. When studying the performance of weighted subspace based techniques this window procedure complicates both the analysis and the implementation. In this paper an attempt is made to clarify the theoretical and numerical difficulties when applying weighting in such scenarios
|
|
