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Performance analysis of least mean square sample matrix inversion algorithm for smart antenna system

机译:智能天线系统最低均方样本矩阵反演算法的性能分析

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Beamforming is a signal processing technique that has the ability to direct the main beam toward signal-of-interest and the null toward signal-of-not-interest without moving antenna array. This technique is achieved by using algorithms which change the amplitude and phase of array pattern continuously. This paper presents hybrid algorithm that is a combination of two algorithms, least mean square algorithm and sample matrix inversion algorithm. The hybrid algorithm (least mean square/sample matrix inversion) is applied on an array of dipoles to overcome the shortcomings of existing algorithms for a robust smart antenna system. To focus the main beam toward the desired direction and place the null in the direction of the interference signals, the weights of the inversion matrix in sample matrix inversion are calculated and these weights will be the initial weights in least mean square algorithm. The merit of this approach solves the convergence speed problem of the least mean square algorithm as well as the computation intensive exists in sample matrix inversion algorithm and decreases the least mean square error. The simulation results indicate the analyses of least mean square, sample matrix inversion and hybrid algorithm performances .These techniques are compared and verified using MATLAB.
机译:波束成形是一种信号处理技术,其能够将主光束引导到兴趣信令和空向非兴趣的信号而不移动天线阵列。通过使用连续改变阵列图案的幅度和相位的算法来实现该技术。本文呈现了混合算法,其是两种算法,最小均方算法和样本矩阵反演算法的组合。混合算法(最小均方/样本矩阵反转)应用于偶极子阵列,以克服稳健的智能天线系统的现有算法的缺点。为了将主光束聚焦到所需方向并将空在干扰信号的方向上放置,计算样本矩阵反转中的反转矩阵的权重,并且这些权重将是最小均方算法的初始权重。该方法的优点解决了最低均方算法的收敛速度问题以及样本矩阵反转算法中的计算密集并降低了最小均方误差。仿真结果表明,最小均方,样本矩阵反转和混合算法性能的分析。比较和使用MATLAB进行验证这些技术。

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