自适应阵列中多级维纳滤波器的有效实现算法
Efficient Algorithms for Implementing Multistage Wiener Filter in Adaptive Arrays
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摘要: 在分析多级维纳滤波器实现算法的基础上，证明了由相关相减算法实现的多级维纳滤波器是一种酉多级维纳滤波器，与Goldstein、Reed和Scharf提出的原始实现算法相比，酉多级维纳滤波器具有更好的降秩性能。该文对相关相减算法中的阻塞矩阵进行改进，使多级维纳滤波器前向递推中观测数据向量的维数逐步降低，且同样能应用于相关相减算法结构。新的实现算法在进一步降低计算量的同时，得到与相关相减算法几乎相同的性能。仿真结果证明了该算法的有效性。
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关键词:
- 自适应阵列;多级维纳滤波器;降秩处理
Abstract: Based on the analysis of the algorithms for implementing Multistage Wiener Filter (MWF), the MWF implemented by the Correlation Subtraction Algorithm (CSA) is proved to be an Unitary MWF (UMWF). The rank reduction performance of UMWF is superior to the original MWF proposed by Goldstein, Reed, and Scharf. In this paper, the block matrixes in the CSA are modified to reduce the size of the observation data vectors step by step in the forward recursion of MWF. The modified block matrixes can also be used in the CSA architecture. The new implementing algorithm needs a lower computation complexity, while keeping almost the same performance as the CSA. The validity of the proposed algorithm is proved by the simulation results. -
Goldstein J S, Reed I S, Scharf L L. A multistage representation of the Wiener filter based on orthogonal projections[J].IEEE Trans. on Information Theory.1998, 44 (7):2943-[2]Ricks D C, Goldstein J S. Efficient architectures for implementing adaptive algorithms. Proceedings of the 2000 Antenna Applications Symposium, Allerton Park, Monticello, IL, Sept. 2000: 29.41.[3]Ricks D C, Cifuentes P G, Goldstein J S. Adaptive beamforming using multistage Wiener filter with a soft stop. Conference Record of the Thirty.Fifth Asilomar Conference on Signals, Systems Computers, Pacific Grove, CA, USA, Nov. 2001:14011.406.[4]Weippert M E, Hiemstra J D, Goldstein J S, et al.. Insights from the relationship between the multistage Wiener filter and the method of conjugate gradients. 2nd IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2002), Rosslyn VA, August 2002: 388.392.[5]Joham M, Zoltowski M D. Interpretation of the multi-stage nested Wiener filter in the Krylov subspace framework. Tech. Rep. TUM-LNS-TR-00-6, Munich University of Technology, November 2000. Also: Technical Report TR-ECE-00.51, Purdue University. -
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