利用导向向量旋转和联合迭代优化的自适应波束形成算法研究
发布时间：2017-12-30 19:31
本文关键词：利用导向向量旋转和联合迭代优化的自适应波束形成算法研究 出处：《声学学报》2016年03期 论文类型：期刊论文
更多相关文章： 自适应波束形成 导向误差 降维算法 变换矩阵 导向矢量 子空间 干扰方向 算法设计 迭代优化 最小方差
【摘要】：针对大型阵列中自适应波束形成技术的实时性和鲁棒性问题,基于最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)波束形成的信号模型框架,提出一种通过对导向矢量进行处理以降低干扰的自适应波束形成算法——稳健联合迭代优化-导向自适应(Robust Joint Iterative Optimization-Direction Adaptive,RJIO-DA)算法。在联合迭代优化的基础上,将降维变换矩阵的每一个列向量看作独立的方向向量,引导子空间内每一个维度上的权值迭代,同时旋转导向向量,减小了由于导向误差的不确定性而导致的性能下降。仿真实验结果表明,与现有的降维算法相比,RJIO-DA算法计算复杂度低、收敛率高、鲁棒性好,可在期望方向上稳健地聚集波束,更好地形成干扰方向的自适应零陷。
[Abstract]:According to the adaptive large array beamforming in real-time and robustness problems, based on the minimum variance distortionless response (Minimum Variance Distortionless Response, MVDR) signal model framework of beamforming, proposes a robust joint iterative optimization - oriented adaptive beam through processing the steering vector to reduce the interference of the algorithm (Robust Joint Iterative Optimization-Direction Adaptive RJIO-DA) algorithm. Based on joint iterative optimization, each column vector dimensionality reduction transformation matrix as independent direction vector, guide weight iterative each dimension sub space, while rotating the steering vector, reduce the performance due to the uncertainty of the orientation error drop. Simulation results show that compared with the existing dimension reduction algorithms, RJIO-DA algorithm has low computational complexity, high convergence rate, The robustness is good, and the beam can be steadily gathered in the desired direction, and the adaptive zero subsidence of the direction of interference can be better formed.
【作者单位】： 丽水学院工程与设计学院;浙江工业大学信息工程学院;
【基金】：国家科技支撑计划课题(2013BAF07B03) 浙江省自然科学基金(LY13F010009)资助
【分类号】：TN911.7
【正文快照】： 自适应波束形成技术是阵列信号处理领域的关键技术之一,广泛应用于雷达、通信、声呐等领域W。为获得较高的阵列X椧婧徒险靼甑牟ㄊ，
本文编号：1356167
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