Probability Density Function Estimators Applied To Non-Stationary Signals - Archive ouverte HAL
Communication Dans Un Congrès Année : 2012

Probability Density Function Estimators Applied To Non-Stationary Signals

Résumé

Abstract This paper studies the influence of the use of finite wordlength on the operation of the LLMS adaptive beamforming algorithm. The convergence behavior of LLMS algorithm, based on the minimum mean square error (MSE), is analyzed for operation with finite precision. Computer simulation results verify that a wordlength of eight bits is sufficient for the LLMS algorithm to achieve performance close to that provided by full precision. Based on the simulation results, it is shown that the LLMS algorithm outperforms least mean square (LMS) in addition to other earlier algorithms, such as, modified robust variable step size (MRVSS) and constrained stability LMS (CSLMS). Keywords -- LLMS algorithm, array beamforming, fixed-point arithmetc.
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Dates et versions

hal-00802919 , version 1 (20-03-2013)

Identifiants

  • HAL Id : hal-00802919 , version 1

Citer

Srar Jalal, Chung Kah, Ali Mansour. Probability Density Function Estimators Applied To Non-Stationary Signals. 20th Telecommunications Forum TELFOR 2012, Nov 2012, Belgrade, Serbia. ⟨hal-00802919⟩
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