Übersetzung für "Maximum likelihood estimation" in Deutsch

Viterbi equalizers are MLSE (Maximum Likelihood Sequence Estimation) sequence estimators.

In particular, the maximum likelihood sequence estimation algorithm and the maximum a posteriori algorithm are suitable for this.

This approach in the time domain leads to a tree-based maximum-likelihood sequence estimation of exponentially increasing complexity.

In particular, the decoding device may be configured as a maximum likelihood sequence estimation decoder or as a maximum a posteriori decoder.

For example, if an optimum or sub-optimum equalizer according to the principle of maximum likelihood sequence estimation or sequential decoding is implemented in the signal processing means, a variation of the parameters of "number of states", "number of considered channel coefficients for computing the metric", "number of followed data paths" and "length of the registers for storing the followed data paths" may produce a simple channel-dependent adjustment of the signal processing means.

These two quadrature components are applied to a Viterbi detector, which recovers the transmitted data in accordance with the Maximum Likelihood Sequence Estimation method (MLSE).

In this connection, attention is to be invited to the dissertation by Jurg Ruprecht, "Maximum-Likelihood Estimation of Multipath Channels", Diss.

In maximum likelihood estimation, we assign towards our probability the quotient of the count of this specific event over all events in our data set.

It is argued that Dodgson's work on cycles anticipates a stochastic model proposed by Thompson and Remage in 1964 and includes ideas that are basic to maximum likelihood estimation.

The text below will explain in more detail the algorithm for processing a reception word z, as is described for example as “Maximum Likelihood Sequence Estimation” algorithm (MLSE) thoroughly in “Optimum And Sub-Optimum Detection Of Coded Data Disturbed By Time-Varying Intersymbol Interference”, Wolfgang Koch and Alfred Baier, 1990 IEEE.

It is known that the perturbing influence of the transmission channel can be reduced by devices at the receiver side, such as equalizers or maximum likelihood sequence estimation.

The proposed maximum-likelihood estimation of the received data sequences using a Viterbi algorithm not on the symbol sequences but on the received edges has the consequence that a model can be used whose number of states is smaller by a factor of M in comparison to the classical equalizer.

In this context, the maximum-likelihood estimation is based upon a maximisation of the likelihood function, which, via an inverse exponential function, minimizes the modulus error squared between the measured, noise-contaminated received signal and a model, ideal, noise-free transmission signal containing the desired timing offset over an observation period.

In principle, the maximum-likelihood estimation can be implemented, within the framework of a simplified model of the clock-pulse recovery, via a limited number of symbols in an infinitely long observation period T 0 or, within the framework of a more realistic model of clock-pulse recovery, via an unlimited number of symbols in a limited observation period T 0 .

This may be effected using statistical methods known from the prior art, such as, by way of example, the maximum likelihood sequence estimation method, the maximum a posteriori method, or other methods suitable for this.

The maximum likelihood sequence estimation method generally constitutes an estimation method for given statistics for determining parameters from a given sample of a random variable, in accordance with this method the values of the parameters being determined in such a way that a so-called “likelihood function” takes on a maximum.

Illustratively, in the maximum likelihood sequence estimation method, the likeliest development of a system is determined on the basis of initial or boundary conditions.

The accumulative current flows of the row lines 701 and of the column lines 702 which are fed to the decoding device 704 are evaluated in the decoding device 704, an estimation of the likeliest activity of the sensor arrays 703 being carried out using the maximum likelihood sequence estimation algorithm.

On the basis of the functionality of the decoding device 704 configured as a maximum likelihood sequence estimation decoder, from the accumulative current flows a decision is taken computationally about the presence or absence of a sensor event at the sensor arrays 703 which, from all the calculated constellations, in accordance with the maximum likelihood sequence estimation method, correspond the most probably to the actual sensor events.

The inverse problem is formulated in the statistical framework of the maximum-likelihood estimation method which enables one to account for errors of the measurements and for errors of model output.