Modelagem tensorial para estimação de parâmetros em arranjos de antenas polarimétricas - Paiva, Jordan Silva de (2014)
In this dissertation, we propose methods based on tensor signal processing for the parameter estimation in electric vector (Tripole) antenna arrays, considering different structures of arrays (ULA, L-shape and UPA). Initially, using a L-shape array, we develop a third order (3-D) tensor model for the received data. Based on this model, a trilinear alternating least squares (T-ALS) algorithm is used for the blind estimation of the source’s parameters. Then, under supervised transmission an alternative method is proposed by resorting to the SVD decomposition, which is compared to the T-ALS algorithm. A second approach is proposed, which is based on a uniform planar array antenna (UPA). In this case a fourth-order (4-D) tensor model is obtained, and the Q-ALS (Quadrilinear Alternating Least Squares) algorithm is used for parameter estimation. An alternative method is also proposed, which exploits the factorization of the Khatri-Rao product. Considering the supervised case, a new algorithm called Nested-SVD is proposed and a comparative study with Q-ALS, T-ALS and SVD algorithms is carried out. The performance of the proposed methods is evaluated through Monte Carlo simulations in different scenarios and array settings. Finally, computational modeling of electric tripole using the high frequency simulation software (HFSS) was performed, enabling the extraction of the L-shape and UPA spatial array gain. Then, the performance of the proposed tensor methods is evaluated in a more realistic scenario, and compared to idealized omnidirectional and unitary gain antenna array models