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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


Papers
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Proceedings ArticleDOI
13 Oct 2002
TL;DR: Analysis of the experimental results proved that the proposed LS-SVM approach to short-term electric load forecasting can achieve greater forecasting accuracy than the traditional model.
Abstract: This paper presents a least squares support vector machines (LS-SVM) approach to short-term electric load forecasting (STLF). The proposed algorithm is more robust and reliable as compared to the traditional approach when actual loads are forecasted and used as input variables. In order to provide the forecasted load, the LS-SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that this approach can achieve greater forecasting accuracy than the traditional model.

50 citations

Journal ArticleDOI
TL;DR: Recursion least squares and least mean squares subspace-based adaptive algorithms in order to identify the impulse response of the multipath channel and demonstrate the improved performance of these methods as compared with the already-existing techniques in the literature.
Abstract: The problem of blind adaptive channel estimation in code-division multiple access (CDMA) systems is considered. Motivated by the iterative power method, which is used in numerical analysis for estimating singular values and singular vectors, we develop recursive least squares (RLS) and least mean squares (LMS) subspace-based adaptive algorithms in order to identify the impulse response of the multipath channel. The schemes proposed in this paper use only the spreading code of the user of interest and the received data and are therefore blind. Both versions (RLS and LMS) exhibit rapid convergence combined with low computational complexity. With the help of simulations, we demonstrate the improved performance of our methods as compared with the already-existing techniques in the literature.

50 citations

Journal ArticleDOI
TL;DR: In the presence of frequency selective crosstalk, an extension of CTC-DPD which can pre-cancel such cros stalk, performs considerably better than CO-D PD, which cannot model the frequency selectivity of the crosStalk.
Abstract: This paper presents a comparative study of adaptive algorithms for digital predistortion (DPD) in multiple antenna transmitters. Crossover predistorter (CO-DPD) and crosstalk canceling predistorter (CTC-DPD) were proposed to overcome the deleterious effect of RF crosstalk before the power amplifiers (PA) on digital predistortion (DPD) in multiple antenna transmitters. This paper discusses the linearization performance and computational complexity of least mean square (LMS) and recursive least squares (RLS) adaptive algorithms for CO-DPD and CTC-DPD. The adaptive predistortion algorithms for a single antenna transmitter can be extended for CO-DPD, by incorporating the DPD coefficients of more than one branch in to one filter coefficient vector of the adaptive algorithm. The adaptive CTC-DPD involves predistorters for each transmitter running in parallel with the adaptive algorithms that track the coupling between the transmitters. The computational complexity of CTC-DPD is considerably lower compared to CO-DPD, as it has lesser predistorter branches. It is estimated that the number of computations needed per sample duration, for the real-time operation of the adaptive CTC-DPD is 47% lesser compared to CO-DPD for a two-antenna transmitter, when a 9th order memory polynomial with 3 memory taps was used. The linearization performances of these adaptive predistortion techniques for two and four antenna transmitters are evaluated through simulations using QPSK, 16-QAM, UMTS, and LTE signals. It is observed that CTC-DPD performs approximately identical to CO-DPD in all the examined cases. In the presence of frequency selective crosstalk, an extension of CTC-DPD which can pre-cancel such crosstalk, performs considerably better than CO-DPD, which cannot model the frequency selectivity of the crosstalk.

50 citations

Journal ArticleDOI
TL;DR: In this article, an affine structure with additional assumptions is considered, in particular, Toeplitz and Hankel structured, noise free and unstructured blocks are allowed simultaneously in the augmented data matrix, and an equivalent optimization problem is derived that has as decision variables only the estimated parameters.

50 citations

Proceedings ArticleDOI
04 May 2014
TL;DR: The state-space model and the corresponding adaptive algorithm for the partitioned-block filtering structure allows for the use of significantly longer filter lengths in comparison to previous work, and for the flexible design and implementation of acoustic echo cancellers for widely differing acoustic conditions.
Abstract: Acoustic echo cancellation has traditionally employed basically all variants known from deterministic adaptive filter design, such as least mean-square (LMS), recursive least-squares (RLS), and frequency-domain adaptive filters (FDAF). More recently, a stochastic adaptive filter design based on the concept of acoustic state-space modeling of the echo path has been introduced to accommodate for an ever sought unification of adaptive filtering and adaptation control. The corresponding Kalman filter theory has been formulated for single-channel, multi-channel, and nonlinear echo cancellation problems. This paper closes an important gap by formulating the state-space model and the corresponding adaptive algorithm for the partitioned-block filtering structure which is especially relevant in practice. This structure allows for the use of significantly longer filter lengths in comparison to previous work, and for the flexible design and implementation of acoustic echo cancellers for widely differing acoustic conditions.

50 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237