自适应频率和二维波达方向角跟踪估计算法

运用特征子空间分析方法的关键问题在于信号或噪声子空间的估计。在实际中有些信号的统计特性通常是随时间变化的，这时需要随时根据新的阵列接收数据对信号或噪声子空间进行更新，以得到参数的实时估计值。在该文中建立了多维信号参量联合估计的3D Unitary ESPRIT算法，然后提出了基于球面平均ULV分解的子空间跟踪算法，将子空间跟踪算法与多维信号参量联合估计算法相结合，得到多维时变信号参数的跟踪估计算法。仿真计算结果验证了该算法的有效性。

Adaptive frequency and 2D DOA estimation tracking

The key problem of eigen-subspace methods is the estimation of signal or noise sub-space. In practical situations, there exist signals whose statistic characteristics always change over time. To obtain the real-time estimates of signal parameters, it is necessary to update the signal/noise subspace according to newly received array sampled output. In this paper, 3D Unitary ESPRIT algorithm is proposed to achieve the combined estimation of 2D DOA and carrier frequency of impinging signals, then a subspace tracking algorithm based on spherically averaged ULV decomposition is presented. With combination of the above subspace tracking algorithm with 3D Unitary ESPRIT algorithm, adaptive 3D Unitary ESPRIT algorithm .is presented to track the time-varying multidimensional parameter estimates. Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.