基于互相关矩阵的二维传播算子实值算法

Real-Valued Two-Dimensional Propagator Method Using Cross-Correlation Matrix

  • 摘要: 为提高传播算子算法在低信噪比下的波达方向(direction of arrival,DOA)估计性能,降低计算复杂度,提出了一种基于互相关矩阵的二维传播算子DOA估计实值算法(UC-PM).该算法通过构造新的互相关矩阵代替阵列接收数据矩阵,抑制了噪声分量的影响,并且保持了传播算子算法计算量小的优点,利用线性运算代替特征分解求得旋转不变关系矩阵.同时,为进一步降低算法计算量,利用酉变换思想构建新的实数域旋转不变关系,将特征分解和最小二乘问题实数化.仿真结果和计算复杂度分析表明,新算法在低信噪比下的估计性能优于传统二维传播算子算法,接近于二维ESPRIT算法,且其计算复杂度远小于二维ESPRIT算法,实时性好,具有良好的实用价值.

     

    Abstract: In order to improve the accuracy of direction of arrival(DOA) estimation and decrease calculation capacity with low SNR, a new real-valued propagator method(PM) for 2-D DOA estimation algorithm using cross-correlation matrix (UC-PM) was proposed. Instead of array received data, cross-correlation matrix was constructed to suppress the effect of noise, and eigen-decomposition was replaced by a linear operator with low calculation capacity. Meanwhile, for reducing the complexity further, a new real-valued rotational invariance matrix was constructed to change eigen-decomposition and total least problems into real ones by unitary transformation. The simulation results showed that, being similar performance with 2-D ESPRIT algorithm, the performance of UC-PM is better than conventional PM algorithm with low SNR, and it has much lower calculation capacity than 2-D ESPRIT algorithm, which made the proposed algorithm of high practical value.

     

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