1,440 research outputs found
Study of L0-norm constraint normalized subband adaptive filtering algorithm
Limited by fixed step-size and sparsity penalty factor, the conventional
sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms
suffer from trade-off requirements of high filtering accurateness and quicker
convergence behavior. To deal with this problem, this paper proposes variable
step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system
identification. We first analyze mean-square-deviation (MSD) statistics
behavior of the L0-NSAF algorithm innovatively in according to a novel
recursion form and arrive at corresponding expressions for the cases that
background noise variance is available and unavailable, where correlation
degree of system input is indicated by scaling parameter r. Based on
derivations, we develop an effective variable step-size scheme through
minimizing the upper bounds of the MSD under some reasonable assumptions and
lemma. To realize performance improvement, an effective reset strategy is
incorporated into presented algorithms to tackle with non-stationary
situations. Finally, numerical simulations corroborate that the proposed
algorithms achieve better performance in terms of estimation accurateness and
tracking capability in comparison with existing related algorithms in sparse
system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure
Novi normirani pojasni adaptivni filtar s promjenjivom duljinom koraka
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in themean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update normalized subband adaptive filter (VSS-SPU-NSAF) is proposed. In this algorithm the filter coefficients are partially updated in each subband at every iteration. Simulation results show the good performance of the proposed algorithms in convergence speed and steady-state MSD.U ovom radu prikazan je novi algoritam za normirani adaptivni filtar s promjenjivim korakom. Kod predloženog filtra, veličina koraka mijenja se kako bi se dobilo najveće smanjenje srednje vrijednosti odstupanja za uzastopne iteracije. Kako bi se smanjila računska složenost filtra, predložen je normirani pojasni adaptivni filtar s promjenjivim korakom i selektivnim parcijalnim osvježavanjem. Kod tog algortima koeficijenti filtra parcijalno se osvježavaju u svakom pojasu i pri svakoj iteraciji. Simulacijski rezultati pokazuju dobru brzinu konvergencije i malu srednju vrijednost odstupanja u stacionarnom stanju za predloženi filtar
Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs
When the input signal is correlated input signals, and the input and output
signal is contaminated by Gaussian noise, the total least squares normalized
subband adaptive filter (TLS-NSAF) algorithm shows good performance. However,
when it is disturbed by impulse noise, the TLS-NSAF algorithm shows the rapidly
deteriorating convergence performance. To solve this problem, this paper
proposed the robust total minimum mean M-estimator normalized subband filter
(TLMM-NSAF) algorithm. In addition, this paper also conducts a detailed
theoretical performance analysis of the TLMM-NSAF algorithm and obtains the
stable step size range and theoretical steady-state mean squared deviation
(MSD) of the algorithm. To further improve the performance of the algorithm, we
also propose a new variable step size (VSS) method of the algorithm. Finally,
the robustness of our proposed algorithm and the consistency of theoretical and
simulated values are verified by computer simulations of system identification
and echo cancellation under different noise models
A subband Kalman filter for echo cancellation
This thesis involves the implementation of a Kalman filter for the application of echo cancellation. This particular Kalman filter is implemented in the frequency domain, in subbands, so as to make it faster and of lesser calculational complexity for real time applications. To evaluate the functioning of this subband Kalman filter, comparison of the performance of the subband Kalman filter is done with respect to the original time domain Kalman filter, and also with other subband adaptive filters for echo cancellation such as the NLMS filter. Additionally, since background noise affects the working of any adaptive filter, the newly developed subband Kalman filter\u27s performance at different noise conditions is compared, and an attempt to keep track of and predict this noise is also performed --Abstract, page iii
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