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Adaptive Constrained DCT-LMS Time Delay Estimation AlgorithmJian, Jiun-Je 27 June 2000 (has links)
n the problem of time delay estimation (TDE), the desired source signals of interest are
correlated and with a specific spectral distribution. In such cases, the convergence speed using
the conventional approaches, viz., time domain adaptive constrained and unconstrained LMS
TDE algorithms, becomes slowly and the performance of TDE will be degraded, dramatically.
In fact, the convergence rate depends highly on the distribution of spectral density of the
desired signal sources. Also, the performance of TDE is affected by the background noises,
accordingly.
To circumvent the problem described above, in this thesis, a transformed domain adaptive
constrained filtering scheme, refers to the constrained adaptive DCT-LMS algorithm, for TDE
is devised. We show that this new proposed constrained algorithm, with the so-called direct
delay estimation formula, for non-integer TDE does perform better than the conventional time
domain adaptive constrained and unconstrained LMS TDE algorithms and the unconstrained
adaptive DCT-LMS TDE algorithm.
Finally, to further reduce the spread of eigenvalue in the unconstrained adaptive
DCT-LMS algorithm, the Gram-Schmidt orthogonalizer approach realizing by the adaptive
Escalator is investigated. It indicates that bias of TDE will occur without using the constraint
of weight vector. That is, it could not be used to alleviate the effect due to background noises.
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Transform-Domain Adaptive Constrained Filtering Algorithms for Time Delay EstimationHou, Jui-Hsiang 27 June 2002 (has links)
The convergence speed using the conventional approaches, viz., time-domain adaptive constrained and unconstrained LMS algorithms, becomes slowly, when dealing with the correlated source signals. In consequence, the performance of time delay estimation (TDE) will be degraded, dramatically. To improve this problem, the so-called transform-domain adaptive constrained filtering scheme, i.e., the adaptive constrained discrete-cosine-transform (DCT) LMS algorithm, has been proposed in [15]. However, the use of any one orthogonal transform will not result in a completely diagonal the input signal auto-correlation matrix for all types of input signals. In fact, the significant non-diagonal entries in the transform-domain auto-correlation matrix, will deteriorate the convergence performance of the algorithm.
To further overcome the problem described above, in this thesis, a modified approach, referred as the adaptive constrained modified DCT-LMS (CMDCT-LMS) algorithm, is devised for TDE under a wide class of input processes. In addition, based on the orthogonal discrete wavelet transform (DWT), an adaptive constrained modified DMT-LMS (CMDWT-LMS) algorithm is also devised and applied to the problem of TDE. We show that the proposed two modified constrained approaches for TDE does perform well than the unmodified approaches under different source signal models. Moreover, the adaptive CMDCT-LMS filtering algorithm does perform slightly better than the adaptive CMDWT-LMS filtering algorithm as observed from the simulation results.
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