Direction finding using beamspace root estimator banks
Direction finding using beamspace root estimator banks
复制标题
DOI：
10.1109/78.726831
复制
发表时间：
1998-11
期刊：
影响因子：
--
通讯作者：
A. Gershman
中科院分区：
文献类型：
--
作者：
A. Gershman
基于特征结构技术在波束空间和根实现上的优越性能和较低的计算复杂度，提出了一种伪随机联合估计策略的波束空间根修正算法(PR-JES)。PR-JES的本质是为给定的样本协方差或数据矩阵生成基于特征结构的估计器库。PR-JES适当地结合“并行”基础估计器的结果，去除了异常值，提高了阈值性能。在非均匀阵列的情况下，利用内插阵列方法来实现根基技术的应用。仿真结果表明，所提出的波束空间根实现方法的性能明显优于谱元素空间PR-JES方法，性能与随机最大似然(ML)方法相近或更好。
Motivated by the superior performance and reduced computational complexity of beamspace and root implementations of eigenstructure techniques, a beamspace root modification of the pseudorandom joint estimation strategy (PR-JES) is developed. The essence of the PR-JES is to generate the eigenstructure-based estimator bank for a given sample covariance or data matrix. Appropriately combining the results of "parallel" underlying estimators, the PR-JES removes outliers and improves the threshold performance. In the case of a nonuniform array, the interpolated array approach is exploited to enable the application of root underlying techniques. Simulations show that the proposed beamspace root implementation outperforms spectral elementspace PR-JES significantly and achieves a performance similar to or better than that of the stochastic maximum likelihood (ML) method.