Performance Analysis of Radiation Patterns Using MVDR Algorithm and DREC Method

For a 16-element uniform linear array, radiation pattern performance analysis can be conducted using both MVDR algorithm and DREC method for comparison. These two approaches possess distinct characteristics in beamforming and signal processing, enabling comprehensive performance evaluation of the array from different perspectives.

The MVDR (Minimum Variance Distortionless Response) algorithm is a classical beamforming method that optimizes radiation pattern gain by minimizing noise and interference power. It demonstrates excellent interference suppression capabilities but shows sensitivity to steering vector errors. The method computes the inverse of the covariance matrix and adjusts weighting coefficients to enhance gain in the desired signal direction while suppressing interference from other directions. In code implementation, this typically involves calculating the sample covariance matrix R_xx = X*X'/N from received data matrix X, then computing the optimal weights w = R_xx^(-1)*a/(a'*R_xx^(-1)*a) where a represents the steering vector.

The DREC (Directional Pattern Constrained Eigenvalue Decomposition) method employs eigenvalue decomposition techniques to analyze array received signals. This approach first computes the covariance matrix, then performs eigenvalue decomposition. The eigenvectors corresponding to larger eigenvalues form the signal subspace, while those associated with smaller eigenvalues constitute the noise subspace. By leveraging signal subspace information, DREC effectively suppresses noise and interference while enhancing gain in the signal direction. The algorithm implementation typically involves [U,S,V] = svd(R_xx) for singular value decomposition, where the diagonal matrix S contains eigenvalues, and U contains eigenvectors.

Regarding performance evaluation, the MVDR algorithm generally offers higher resolution and superior interference suppression capability, but it suffers from higher computational complexity and greater sensitivity to array errors. In comparison, the DREC method adapts better to variations in noise and signal environments through eigenvalue decomposition, though it also requires substantial computational resources. Both methods' radiation pattern performance can be compared using metrics such as main lobe width, sidelobe level, and interference suppression capability.

By comparing these two methods, we can conduct a more comprehensive evaluation of uniform linear array radiation pattern performance, providing optimization basis for beamforming and interference suppression in practical applications. The comparative analysis might include plotting radiation patterns using pattern plotting functions and evaluating performance metrics through quantitative measurements of beamwidth and sidelobe attenuation.