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

机译:智能天线系统最小均方样本矩阵求逆算法的性能分析

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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,能够在不移动天线阵列的情况下将主波束引向感兴趣的信号,将零波束引向不感兴趣的信号。该技术是通过使用连续改变阵列图案的幅度和相位的算法来实现的。本文提出了一种混合算法,它是两种算法的结合,最小均方算法和样本矩阵求逆算法。将混合算法(最小均方/样本矩阵求逆)应用于偶极子阵列,以克服现有算法对鲁棒智能天线系统的缺点。为了将主光束聚焦到所需方向并将零点放置在干扰信号的方向上,计算样本矩阵求逆中求逆矩阵的权重,这些权重将成为最小均方算法的初始权重。该方法的优点是解决了最小均方算法的收敛速度问题以及样本矩阵求逆算法中计算量大的问题,并减小了最小均方误差。仿真结果表明了对最小均方,样本矩阵求逆和混合算法性能的分析。使用MATLAB对这些技术进行了比较和验证。

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