260 research outputs found
Efficient Robust Adaptive Beamforming Algorithms for Sensor Arrays
Sensor array processing techniques have been an important research area in recent years.
By using a sensor array of a certain configuration, we can improve the parameter estimation
accuracy from the observation data in the presence of interference and noise. In this
thesis, we focus on sensor array processing techniques that use antenna arrays for beamforming,
which is the key task in wireless communications, radar and sonar systems.
Firstly, we propose a low-complexity robust adaptive beamforming (RAB) technique
which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch
Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance
matrix of the input data and the interference-plus-noise covariance (INC) matrix
by using the Oracle Approximating Shrinkage (OAS) method. Secondly, we present
cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms.
The proposed algorithms are based on the exploitation of the cross-correlation
between the array observation data and the output of the beamformer. Thirdly, we propose
distributed beamforming techniques that are based on wireless relay systems. Algorithms
that combine relay selections and SINR maximization or Minimum Mean-Square-
Error (MMSE) consensus are developed, assuming the relay systems are under total relay
transmit power constraint. Lastly, we look into the research area of robust distributed
beamforming (RDB) and develop a novel RDB approach based on the exploitation of
the cross-correlation between the received data at the relays and the destination and a
subspace projection method to estimate the channel errors, namely, the cross-correlation
and subspace projection (CCSP) RDB technique, which efficiently maximizes the output
SINR and minimizes the channel errors. Simulation results show that the proposed
techniques outperform existing techniques in various performance metrics
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
- …