| 21. |
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| 22. |
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| 23. |
- Gustavsson, Jan-Olof, et al.
(författare)
-
Simultaneous Channel and Symbol Maximum Likelihood Estimation in Laplacian Noise
- 1998
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Ingår i: [Host publication title missing]. - Beijing : IEEE, Piscataway, NJ, USA. - 0780343255 ; , s. 81-84
-
Konferensbidrag (refereegranskat)abstract
- This paper treats channel estimation and signal detection in Laplacian noise. The received signal is assumed to be a transmitted signal which has been corrupted by an unknown channel, modeled as a FIR filter, the output being further disturbed by additive independent Laplacian noise. The transmitted signal is assumed to depend on an unknown parameter belonging to a known finite set. The simultaneous maximum likelihood (ML) estimator of the unknown parameter, as well as of the FIR filter coefficients, is derived. The ML estimate of the channel can be obtained by using a linear programming approach and the decision about the parameter is based on the output from a set of generalized matched filters. Simulation results are included in order to illustrate the performance of the proposed receivers.
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| 24. |
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| 25. |
- Nordebo, Sven, et al.
(författare)
-
A Well-Conditioned Quadratic Program for Unique Design of Two Dimensional Weighted Chebyshev FIR Filters
- 1996
-
Konferensbidrag (refereegranskat)abstract
- The weighted Chebyshev design of two-dimensional FIR filters is in general not unique since the Haar condition is not generally satisfied. However, for a design on a discrete frequency domain, the Haar condition might be fulfilled. The question of uniqueness is, however, rather extensive to investigate. It is therefore desirable to define some simple additional constraints to the Chebyshev design in order to obtain a unique solution. The weighted Chebyshev solution of minimum Euclidean filter weight norm is always unique, and represents a sensible additional constraint since it implies minimum white noise amplification. It is shown that this unique Chebyshev solution can always be obtained by using an efficient quadratic programming formulation with a strictly convex objective function and linear constraints An example where a conventional Chebyshev solution is non-unique is discussed in the paper.
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| 26. |
- Nordebo, Sven, et al.
(författare)
-
Application of Infinite Dimensional Linear Programming to FIR Filter Design with Time Domain Constraints
- 1998
-
Konferensbidrag (refereegranskat)abstract
- Previously the envelope-constrained filtering problem was formulated as designing an FIR filter such that the filter's L2 norm is minimized subject to the constraint that its response to a specified input pulse lies within a prescribed envelope. In this paper, we recast this filter design problem as a frequency-domain L infinity optimization problem with time-domain constraints. Motivations for solving this problem are given. Then recently developed infinite dimensional linear programming techniques are used for the design of the required FIR filter. For illustration, we apply the approach to a numerical example which deals with the design of an equalization filter for a digital transmission channel.
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| 27. |
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| 28. |
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| 29. |
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| 30. |
- Nordebo, Sven, et al.
(författare)
-
Minimum Norm Design of Two-Dimensional Weighted Chebyshev FIR Filters
- 1997
-
Ingår i: IEEE transactions on circuits and systems. 2, Analog and digital signal processing (Print). - New York : IEEE Circuits and Systems Society. - 1057-7130 .- 1558-125X. ; 44:3, s. 251-253
-
Tidskriftsartikel (refereegranskat)abstract
- The weighted Chebyshev design of two-dimensional FIR filters is in general not unique since the Haar condition is not generally satisfied. However, fo r a design on a discrete frequency domain, the Haar condition might be fulf illed. The question of uniqueness is, however, rather extensive to investig ate. It is therefore desirable to define some simple additional constraints to the Chebyshev design in order to obtain a unique solution. The weighted Chebyshev solution of minimum Euclidean filter weight norm is always uniqu e, and represents a sensible additional constraint since it implies minimum white noise amplification. This unique Chebyshev solution can always be ob tained by using an efficient quadratic programming formulation with a stric tly convex objective function and linear constraints. An example where a co nventional Chebyshev solution is nonunique is discussed in the brief.
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