3 research outputs found
Enhanced NLMS adaptive array via DOA detection
In various adaptive array applications, the desired-user signal arrives from only a relatively small number of directions. The paper proposes an NLMS-based adaptive algorithm that incorporates a direction of arrival (DOA) detection criterion. The criterion stems from Akaike's information criterion and Donoho's thresholding principle. Within 'low-dimensional' DOA applications, the inclusion of the DOA detection criterion leads to a reduction in the number of NLMS adapted parameters. The result is significantly improved convergence and tracking speeds, as well as improved nulling of multi-user interference signals. Simulations demonstrate the favourable performance of the proposed NLMS adaptive-array system
Enhanced NLMS adaptive array via DOA detection
In various adaptive array applications, the desired-user signal arrives from only a relatively small number of directions. The paper proposes an NLMS-based adaptive algorithm that incorporates a direction of arrival (DOA) detection criterion. The criterion stems from Akaike's information criterion and Donoho's thresholding principle. Within 'low-dimensional' DOA applications, the inclusion of the DOA detection criterion leads to a reduction in the number of NLMS adapted parameters. The result is significantly improved convergence and tracking speeds, as well as improved nulling of multi-user interference signals. Simulations demonstrate the favourable performance of the proposed NLMS adaptive-array system
Contribuições à modelagem estocástica de algoritmos adaptativos normalizados
Tese (doutorado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-Graduação em Engenharia ElĂ©trica, FlorianĂłpolis, 2015.Este trabalho de pesquisa trata da modelagem estocástica de trĂŞs algoritmos adaptativos bem conhecidos da literatura, a saber: o algoritmo NLMS (normalized least-mean-square), o algoritmo IAF PNLMS (individual-activation-factor proportionate NLMS) e o algoritmo TDLMS (transform-domain least-mean-square). Particularmente para o algoritmo NLMS, um modelo estocástico analĂtico Ă© obtido levando em conta um ambiente nĂŁo estacionário e sinais de entrada gaussianos complexos. Baseado nas expressões de modelo, o impacto dos parâmetros do algoritmo sobre o seu desempenho Ă© discutido, evidenciando algumas das caracterĂsticas de rastreamento do algoritmo NLMS frente ao ambiente nĂŁo estacionário considerado. Para o algoritmo IAF-PNLMS, assumindo um ambiente estacionário, um modelo estocástico mais preciso do que os atĂ© entĂŁo disponĂveis na literatura Ă© apresentado, considerando sinais de entrada gaussianos correlacionados tanto complexos quanto reais. Com respeito ao algoritmo TDLMS, um modelo estocástico melhorado Ă© derivado focando em um ambiente nĂŁo estacionário e sinais de entrada gaussianos correlacionados reais. A partir das expressões de modelo obtidas, o impacto dos parâmetros do algoritmo TDLMS sobre o seu desempenho Ă© discutido. Resultados de simulação para diferentes cenários de operação sĂŁo mostrados, confirmando a precisĂŁo dos modelos estocásticos propostos tanto na fase transitĂłria quanto em regime permanente.Abstract : This research work focuses on the stochastic modeling of three well-known adaptive algorithms from the literature, namely: the normalized least-mean-square (NLMS) algorithm, the individual-activation-factor proportionate NLMS (IAF-PNLMS) algorithm, and the transform-domain least-mean-square (TDLMS) algorithm. Particularly for the NLMS algorithm, an analytical stochastic model is obtained taking into account a nonstationary environment and complex-valued Gaussian input data. Based on the obtained model expressions, the impact of the algorithm parameters on its performance is discussed, clarifying some of the tracking properties of the NLMS algorithm vis-Ă -vis the nonstationary environment considered. For the IAF-PNLMS algorithm, assuming a stationary environment, a more accurate stochastic model than those available so far in the literature is presented considering both complex- and real-valued Gaussian correlated input data. Regarding the TDLMS algorithm, an improved stochastic model is derived focusing on a nonstationary environment and real-valued Gaussian correlated input data. From the obtained model expressions, the impact of the TDLMS algorithm parameters on its performance is discussed. Simulation results for different operating scenarios are shown, confirming the accuracy of the proposed stochastic models for both transient and steady-state phases