Array Manifold Errors Research Articles

Polarised beampattern synthesis against array manifold mismatch

The problem of joint synthesis of spatial power pattern and mainlobe radiation polarisation using a vector antenna array is considered in the presence of array manifold errors. A general approach to the synthesis problem is to formulate the restriction on the power and radiation polarisation as a convex optimisation programing. However, for certain types of vector antennas like tripole and electromagnetic vector sensors, the optimal weights might be extremely large and the resultant beampattern could be very sensitive to the array manifold errors. An intuitive explanation on this phenomenon is given based on the subspace theory. A worst case performance optimisation technique- based scheme is presented to draw attention to the polarised beampattern synthesis against the large weights problem and array manifold errors. Numerical examples regarding different types of vector antennas used for one- and two-dimensional polarised beampattern synthesis are provided to validate the efficacy of the scheme.

Robust Fixed Frequency Invariant Beamformer Design Subject to Norm-Bounded Errors

A novel robust fixed frequency invariant beamformer (FIB) against arbitrary array manifold errors is proposed, by employing the worst-case performance optimization technique with norm-bounded error matrices. A closed-form solution is provided by finding the minimum generalized eigenvector of a pair of matrices. Design examples show that our proposed design significantly outperforms previously proposed FIBs in terms of both frequency invariant property and sidelobe attenuation in the presence of array manifold errors.

Robust Wideband Beamforming With Frequency Response Variation Constraint Subject to Arbitrary Norm-Bounded Error

Traditional robust wideband beamforming is mainly achieved by extending the narrowband robust beamforming methods to the wideband case through frequency sampling with robust constraints imposed on different frequency points separately. The resultant beamformer may have different responses at different frequencies, causing intolerable distortion to the desired signal. To improve the frequency response consistency of the beamformer against arbitrary array manifold errors, a novel robust beamformer is proposed with a closed-form solution. A previously proposed robust beamformer can be considered as its special case.

Forward-backward averaging in the presence of array manifold errors

We investigate the use of forward-backward (f/b) averaging for estimating the covariance matrix used for adaptive beamforming and space-time adaptive processing (STAP). We demonstrate that the estimation loss is reduced by the use of f/b averaging and, for some STAP cases, f/b averaging can even quadruple the available sample support. We also show that unknown array manifold errors have little effect on the effectiveness of f/b averaging. The gain from f/b averaging is demonstrated on data from the mountaintop database.

Method for array calibration in high-resolution sensor array processing

With few exceptions, most high-resolution sensor array algorithms for direction of arrival (DOA) estimation are sensitive to the errors in the nominal array manifold. Deviations from the true array manifold, typically resulting from mutual coupling, array channels mismatch and sensor positioning errors, can seriously degrade the algorithms' performance. Several array calibration algorithms have been reported in the literature; however, only a handful have addressed the problem of calibrating the errors in the array manifold that result from the combined effects of mutual coupling, sensor positioning error and mismatch in the array channels. This problem is addressed in the paper. By extending the array parametrisation to describe the deviations of the nominal array manifold from the actual array manifold, the array calibration problem considered can be posed as that of estimating the calibration matrix and sensor positions. The array calibration method proposed estimates the sensor positions and calibration matrix from a set of measured steering vectors. The usefulness and behaviour of the proposed calibration method are illustrated through simulated experiments.

Preprocessing for direction finding with minimal variance degradation

Numerous authors have advocated the use of preprocessing in high-resolution direction of arrival (DOA) algorithms. The benefits cited include reduced computation, improved performance in spatially colored noise, and enhanced resolution. The authors identify the preprocessing matrices that provide minimum variance estimates of DOA for a number of models and algorithms. They examine the Cramer-Rao bound (CRB) for Gaussian signals, the CRB for deterministic signals, and the asymptotic variance of the MUSIC estimator for preprocessed data. They also study the effect of array manifold errors on the direction estimates. As expected, the optimal preprocessor requires knowledge of the source directions. However, they show that performance that is close to optimal can be obtained with only approximate knowledge of the source directions (with an error not exceeding the array beamwidth) if the design rules outlined in this paper are used. >