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In practice, the length of the impulse response of the system to be identified is unknown and often infinite. When the system is modeled as an FIR filter, the length is usually shorter, and hence the name deficient-length filter. The learning rate, mean square error, and other properties of a deficient-length adaptive filter are different from that of a filter that is of sufficient length. In this...
When adaptive altering algorithms are implemented in a finite wordlength processor, quantization effects are inevitable. In this paper, the Euclidean direction search (EDS) altering algorithm is analyzed for quantization effects. A mathematical analysis is performed for both transient and steady state behavior of the algorithm. A new energy conservation identity and an expression for the steady state...
In adaptive filtering, it is typical to assume that the adaptive filter impulse response is of the same length as the impulse response of the unknown system to be identified. However, in practice this is usually not the case. The filter impulse response is typically shorter and hence called deficient length. The Euclidean direction search (EDS) algorithm is a relatively new adaptive filtering scheme,...
This paper reports several observations about stopping and restarting adaptive updates to recursive least-squares lattice (LSL) adaptive filtering algorithms. When updates are stopped, the adaptive filter becomes a fixed filter. Simulation examples demonstrate that large output error results from abruptly stopping or restarting adaptive updates. A remedy to the problem is to transition the adaptive...
This paper considers the problem of prototype filter design for subband adaptive filtering applied to system identification. The minimum mean-squared estimation error (MMSE) depends only the subband analysis filter and the response of the unknown system. We use MMSE as a design criterion to select the best analysis filter response. We show how this can be done independently of the unknown system....
This paper shows that the lowest mean-squared error achievable in a subband adaptive filter depends on the error in a least-squares estimation problem involving the impulse response of the subband analysis filter. This connection is exploited to study the effect upon minimum mean-squared error of varying subband adaptive filter length, fullband time delay, and the subband decimation factor
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