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波束形成自适应算法——样本相关矩阵直接求逆算法(SMI)-Beamforming adaptive algorithm- the sample correlation matrix inversion algorithm directly (SMI)
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块自适应处理算法先有采样快拍数据计算采样协方差矩阵，再来计算自适应权矢量。典型的块自适应处理算法为采样矩阵求逆(SMI)算法。仿真了采样矩阵求逆法(SMI)的波束形成方向图旁瓣的高低受信号快拍数的影响。-Block adaptive processing algorithms prior sample snapshot data of the sampling covariance matrix, again computing adaptive weight vector. A typical
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针对均匀线列阵，当采用样本协方差矩阵求逆的方法时，得到的波束扫描估计方位谱。三个信号源-For uniform linear array, when using the sample covariance matrix inversion method, estimated by scanning the beam position spectrum. Three sources
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algo for making sample matrix inversion response pattern
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基于采样矩阵求逆算法的自适应数字波束形成器程序-Sample matrix inversion algorithm based on adaptive digital beamformer program
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通过设置和实际情况相接近的干扰信号、期望信号以及噪声信号参数，来仿真采样
矩阵求逆算法、单星约束下的最小输出功率算法、单星无约束下的最小输出功率
算法分别于单星信号在单个宽带干扰信号、单个窄带干扰信号和复杂电磁干扰信
号存在时的干扰抑制性能，得出在强干扰条件下算法由优到劣的顺序是采样矩阵
求逆算法、单星约束下的最小输出功率算法和单星无约束下的最小输出功率算法，
弱干扰条件下单星无约束下的最小输出功率算法失效等结论。-By setting and close to the ac
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使用样本协方差矩阵求逆方法仿真的波束图。使用的是均匀线列阵，信号方向为10度，有噪声，没有干扰。-Using the sample covariance matrix inversion method for the simulation beam pattern. Using a uniform linear array, the signal direction of 10 degrees, there is noise, no interference.
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针对样本协方差矩阵求逆方法中采样点小于阵元个数时作出的仿真。此改进方法可以在采样点小于阵元个数时提高波束形成器的性能。-For the sample covariance matrix inversion method of sampling is less than the number of array elements made the simulation. This improved method can be sampled at less than the number of ar
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里面包含两种算法，最小均方误差和采样矩阵求逆算法，算法已经跑过，可以直接用，希望给个好评。-Which contains two algorithms, the minimum mean square error and the sample matrix inversion algorithm, the algorithm has been run through, can be directly used, want to give praise.
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Sample Matrix Inversion is implemented via matlab. the code plot the SINR performance as a function of BER and compare it to the theoretical upper bound BER.
YOU CAN CHANGE THE PARAMETERS AND TEST THE RESULTS.
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We presents a comparative study between the least Mean Square (LMS) and Sample Matrix Inversion (SMI)
Algorithms and their application to the smart antenna optimization.
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We presents a comparative study between the least Mean Square (LMS) and Sample Matrix Inversion (SMI)
Algorithms and their application to the smart antenna optimization.
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