Mouse Lymphocytic Choriomeningitis Virus (LCMV) ELISA Kit

Blind beamforming methods are employed when the geometry of the array and the positions of its antennas are unknown. The existing blind beamforming methods can only tackle with linear and planar arrays. Such methods cannot be simply extended to conformal arrays. In this letter, a novel method for blind beamforming for an arbitrarily shaped conformal array is proposed. By calculating the singular value decomposition (SVD) of the cumulant matrix of the received data, the manifold matrix of the array is first extracted. This matrix determines the direction of the sources and is employed in the linearly constrained minimum variance (LCMV) formulation to obtain the beamforming weights. The individual pattern of each antenna is also incorporated into the LCMV formulation. Our simulation results show that the proposed method can be greatly employed in conformal array blind beamforming and locate directional sources solely based on the received data.

Traditional space-based beamforming is totally conducted on the satellite, which brings lots of problems to the space signal processing. Ground-based beamforming system transfers the beamforming operation to the ground, but we face the problem of too much down link data and too many channels especially in the case of a lot of antenna array elements. The space-ground hybrid beamforming technology combines the advantages of these two projects. First, we adopt FAPI algorithm to extract the subspace of desired signal and interference signals to realize the compression of the matrix, which effectively decreases the amount of down link data. Then, signals transmit through channels to the ground. We use LCMV iteration algorithm to make signals beamformed. This technology not only lightens the space processing burden, but also reduces the number of channels and saves channel resources. According to the simulation results, space dimension reduction operation has no impacts on the performance of the ground adaptive beamforming, which proves the effectiveness of the algorithm.