Techniques for Adaptive Array Beamforming Under Scenario Mismatch
Date Issued
2012
Date
2012
Author(s)
Huang, Chia-Cheng
Abstract
Adaptive array beamforming, which can automatically extract signal of interest (SOI) while suppressing signal not of interest (SNOI) and noise, has received much attention in several application areas. For conventional adaptive array beamforming, the a priori information required for adapting the weights is either the waveform or the direction of the SOI. Over the past two decades, adaptive array beamforming utilizing signal cyclostationarity has been widely presented in the literature. In contrast, the cyclostationarity-exploiting (cyclic) adaptive beamforming techniques do not require any priori information about the waveform or the direction according to the SOI and thus achieve blind adaptive beamforming. The purpose of this dissertation is
mainly to develop several efficient and robust techniques for cyclic adaptive beamforming. We also present two novel robust techniques for conventional adaptive beamforming.
In this dissertation, we consider the cyclic adaptive beamforming in the presence of error due to the effect of using finite data samples. To cope with the finite sample effect, we first present an estimation error model to represent the perturbation due to finite sample effect on the sample cyclic correlation vector. The sample cyclic correlation vector plays a key role required for adapting the weights of the least-squares spectral self-coherence restoral (LS-SCORE) algorithm. Then, two efficient methods, namely the subspace projection and loading-based methods, are proposed to eliminate the perturbation. Moreover, a novel scheme to extend the aforementioned methods to
deal with the situation of multiple SOIs is also presented. To achieve faster convergence rate, we present a new cyclic beamforming method based on the well-known Capon method. In the proposed method, the adaptive weights are obtained by using a constraint related to the signal cyclostationarity and the signal subspace of the received data correlation matrix.
Since the a priori information required by performing cyclic adaptive beamforming is only the cycle frequency of the SOI, we analyze the performance degradation of the cyclic adaptive beamforming in the presence of cycle frequency error (CFE). We present a compensation method to reconstruct the cyclic correlation matrix by using a compensation matrix to cope with the deterioration of its dominant singular value when CFE exists. To further deal with the situation of multiple SOIs with CFE, an efficient robust method is proposed to simultaneously estimate the actual cycle frequencies of the SOIs and its convergence property is also evaluated. On the other hand, we also establish the statistical model of the cyclic correlation vector under random CFE (RCFE). A robust method is developed to tackle the problem due to RCFE and analytical formulas for evaluating the performance of this robust method are further derived.
For a steered-beam adaptive beamformer, the adaptive weights are calculated by minimizing the beamformer''s output power subject to the constraint that forces the array to make a constant response in the direction of the SOI. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering vector of the SOI. To alleviate the difficulty due to steering vector mismatch, we present two variations of the diagonal loading (DL) approaches. The
proposed approaches provide large loading factors for the least significant eigenvalues of the received data correlation matrix and small ones for the most significant eigenvalues. This is a significant advantage over the conventional DL methods and achieves significant robustness against the above mentioned difficulty.
Subjects
Adaptive beamforming
cyclostationary signals
SCORE
CFE
diagonal loading
Type
thesis
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