13 research outputs found
Quadratically Constrained Beamforming Robust Against Direction-of-Arrival Mismatch
It is well known that the performance of the minimum variance distortionless response (MVDR) beamformer is very sensitive to steering vector mismatch. Such mismatches can occur as a result of direction-of-arrival (DOA) errors, local scattering, near-far spatial signature mismatch, waveform distortion, source spreading, imperfectly calibrated arrays and distorted antenna shape. In this paper, an adaptive beamformer that is robust against the DOA mismatch is proposed. This method imposes two quadratic constraints such that the magnitude responses of two steering vectors exceed unity. Then, a diagonal loading method is used to force the magnitude responses at the arrival angles between these two steering vectors to exceed unity. Therefore, this method can always force the gains at a desired range of angles to exceed a constant level while suppressing the interferences and noise. A closed-form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has excellent signal-to-interference-plus-noise ratio performance and a complexity comparable to the standard MVDR beamformer
A Robust Beamformer Based on Weighted Sparse Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe level of a minimum variance distortionless response
(MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in
the beamformer design to provide a lower sidelobe level and deeper nulls for
interference avoidance, as compared with a conventional MVDR beamformer. The
proposed beamformer also shows improved robustness against the mismatch between
the steering angle and the direction of arrival (DOA) of the desired signal,
caused by imperfect estimation of DOA.Comment: 4 pages, 2 figure
Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe of the minimum variance distortionless response (MVDR)
beamformer recently. To further improve the performance, we add a mixed norm
constraint on the beam pattern. It matches the beam pattern better and
encourages dense distribution in mainlobe and sparse distribution in sidelobe.
The obtained beamformer has a lower sidelobe level and deeper nulls for
interference avoidance than the standard sparse constraint based beamformer.
Simulation demonstrates that the SINR gain is considerable for its lower
sidelobe level and deeper nulling for interference, while the robustness
against the mismatch between the steering angle and the direction of arrival
(DOA) of the desired signal, caused by imperfect estimation of DOA, is
maintained too.Comment: 10 pages, 3 figures; accepted by Wireless Personal Communication
Joint temporal-spatial reference beamforming: EIG beamforming
Traditional methods for beamforming can be grouped in two families, the time reference beamformer and the spatial reference beamforming. Nevertheless, the increasing demand on receivers for location systems and high bandwidth over frequency selective fading able to properly manage multipath and co-channel interference motivates the need for versatile processing able to cope with both problems. This paper presents a new beamforming procedure, derived from a ML-like framework, which is able to either remove full coherent arrivals, as well as, to enhance them when required yet preserving the receiver architecture and low design complexity. The performance of the beamformer is also tested in the multiuser broadcast scenario.Peer ReviewedPostprint (published version
Model-Switched Beamformer with Large Dynamic Range
The strong desired signal will be mitigated due to "self-nulling" for the adaptive beamformer, even if the array calibration is used. The proposed methodology switches the models between phased array and adaptive array. In general, the system utilizes Frost adaptive beamforming. However, it will be switched to phased array if the "self-nulling" appears. According to the estimation of the array pattern at the direction of desired signal, we can determine if the "self-nulling" happens. The new approach is much easier to implement compared with the various robust beamforming algorithms
Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint
A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm
Radio Astronomical Image Formation using Constrained Least Squares and Krylov Subspaces
Image formation for radio astronomy can be defined as estimating the spatial
power distribution of celestial sources over the sky, given an array of
antennas. One of the challenges with image formation is that the problem
becomes ill-posed as the number of pixels becomes large. The introduction of
constraints that incorporate a-priori knowledge is crucial. In this paper we
show that in addition to non-negativity, the magnitude of each pixel in an
image is also bounded from above. Indeed, the classical "dirty image" is an
upper bound, but a much tighter upper bound can be formed from the data using
array processing techniques. This formulates image formation as a least squares
optimization problem with inequality constraints. We propose to solve this
constrained least squares problem using active set techniques, and the steps
needed to implement it are described. It is shown that the least squares part
of the problem can be efficiently implemented with Krylov subspace based
techniques, where the structure of the problem allows massive parallelism and
reduced storage needs. The performance of the algorithm is evaluated using
simulations