文件名称:Beamform-ing-Algorithm
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提出一种基于直接数据域最小二乘方法的自适应多波束形成算法,包括前向计算、后向计算和前-
后向计算。利用天线阵元输出复电压的单快拍数据构建矩阵方程,采用共轭梯度法求解得到阵列的自适应权值向
量,从而在所有期望信号方向形成接收波束,同时在各干扰方向形成深零陷,使信干噪比显著提高。由于只需对单
快拍数据进行处理,并且避免了样本协方差矩阵的构造及矩阵求逆运算,故计算复杂度较传统算法低。-An adap tive multip le beamforming algorithm based on D3LS ( direct data domain least squares) app roach is
p resented in this paper, including forward p rocedure, backward p rocedure and forward2backward p rocedure. A single snap2
shot data of the comp lex voltagesmeasured at array elements is used to form the matrix equation, which is solved with the
conjugate gradientmethod to obtain the adap tive weight vector of array. Finally, beams are formed along the DOAs ( direction
of arrival) of SO Is ( signal of interest) and deep nullings are made along the DOAs of interferences to imp rove the SINR ( sig2
nal2to2interference2p lus2noise ratio) greatly. The p roposed algorithm has a lower computation comp lexity than conventional al2
gorithms as it p rocesses only a single snap shot data as opposed to forming the samp le covariance matrix and performingmatrix
inversion operation.
后向计算。利用天线阵元输出复电压的单快拍数据构建矩阵方程,采用共轭梯度法求解得到阵列的自适应权值向
量,从而在所有期望信号方向形成接收波束,同时在各干扰方向形成深零陷,使信干噪比显著提高。由于只需对单
快拍数据进行处理,并且避免了样本协方差矩阵的构造及矩阵求逆运算,故计算复杂度较传统算法低。-An adap tive multip le beamforming algorithm based on D3LS ( direct data domain least squares) app roach is
p resented in this paper, including forward p rocedure, backward p rocedure and forward2backward p rocedure. A single snap2
shot data of the comp lex voltagesmeasured at array elements is used to form the matrix equation, which is solved with the
conjugate gradientmethod to obtain the adap tive weight vector of array. Finally, beams are formed along the DOAs ( direction
of arrival) of SO Is ( signal of interest) and deep nullings are made along the DOAs of interferences to imp rove the SINR ( sig2
nal2to2interference2p lus2noise ratio) greatly. The p roposed algorithm has a lower computation comp lexity than conventional al2
gorithms as it p rocesses only a single snap shot data as opposed to forming the samp le covariance matrix and performingmatrix
inversion operation.
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