改进PASTd算法在大型自适应阵中的应用

矩阵特征分解算法中紧缩近似投影子空间跟踪(PASTd)算法在自适应阵波束形成中得到了广泛应用.在对其仿真中发现仅在信噪比较低时该算法才能得到较好的结果.针对这一缺陷,正交近似投影子空间跟踪(OPAST)算法被引伸到PASTd中.改进算法可在不知道信号维数的情况下估算信号的特征向量与特征值,并保证特征向量的正交性,因此具有更好的收敛性能,而算法复杂度基本不变.改进算法与多重信号分类(MUSIC)算法相结合应用于大型自适应阵,可对主瓣及其附近区域的干扰进行抑制,并大大降低MUSIC算法的计算量,对其干扰零点的形成有很强实用价值.

Modified PASTd algorithm and its application in large-scale adaptive antenna arrays

Projection approximation subspace tracking with deflation (PASTd) algorithm belonging to eigen-decomposition algorithm was widely used in adaptive beam forming for antenna array. However, it has been found that this algorithm can only work satisfactorily when the signal to noise level is low. In order to solve this problem, fast orthonormal PAST (OPAST)algorithm was introduced into PASTd algorithm. The modified algorithm can estimate eigenvector and eigenvalue of useful signals when the dimension of useful signals is unknown. The modified algorithm can insure the orthogonality of the eigenvectors, and the modified algorithm’s astringency is better than PASTd algorithm while the computation complexity almost remains the same. The modified PASTd algorithm combined with multiple signal classification (MUSIC) algorithm applied in large-scale adaptive arrays can restrain the interfaces in the main beam and vicinities of the main beam. The combined algorithm significantly deduces the computation cost of MUSIC and it is also very practical to form deep nulling at interfere direction.