A systolic algorithm for robust adaptive LCMV beamforming with anadjustable constraint 1

نویسندگان

  • Filiep Vanpoucke
  • Marc Moonen
چکیده

This paper presents a novel, fully parallel algorithm for robust linearly constrained minimum variance beamforming. The robustness results from the use of an adjustable constraint to compensate for the unavoidable small mismatch between the nominal and the actual steering vector of the signal-of-interest. The need for parallelism follows from the large computational demands of beamforming algorithms and by the high data rates which are typically present in communications applications. The algorithm exhibits good numerical properties as it is based on orthogonal transformations only. Two systolic architectures are presented on which the throughput is independent of the size of the beamformer.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interferen...

متن کامل

Robust Reduced-Rank Adaptive LCMV Beamforming Algorithms Based on Joint Iterative Optimization of Parameters

This chapter presents reduced-rank linearly constrained minimum variance (LCMV) algorithms based on the concept of joint iterative optimization of parameters. The proposed reduced-rank scheme is based on a constrained robust joint iterative optimization (RJIO) of parameters according to the minimum variance criterion. The robust optimization procedure adjusts the parameters of a rank-reduction ...

متن کامل

Robust Adaptive LCMV Beamformer Based on an Iterative Suboptimal Solution

The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additi...

متن کامل

Adaptive Set-Membership Reduced-Rank Least Squares Beamforming Algorithms

This paper presents a new adaptive algorithm for the linearly constrained minimum variance (LCMV) beamformer design. We incorporate the set-membership filtering (SMF) mechanism into the reduced-rank joint iterative optimization (JIO) scheme to develop a constrained recursive least squares (RLS) based algorithm called JIO-SM-RLS. The proposed algorithm inherits the positive features of reduced-r...

متن کامل

A Robust Adaptive Observer-Based Time Varying Fault Estimation

This paper presents a new observer design methodology for a time varying actuator fault estimation. A new linear matrix inequality (LMI) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (FAFE). The FAFE is capable of estimating a wide range of time-varying actuator fault...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1994