Signal Processing Based On Polarization Sensitive Array

Polarization Sensitive Array (PSA) can acquire the polarization information besides the spatial information of the Electromagnetic signals, which consists of a group of polarization sensitive elements. PSA outperforms the common arrays due to the additional polarization information. PSA possesses strong anti-jamming ability, robust detecting performance, high resolving power and polarization multi-address capability. Therefore, PSA will play an important role in both military and civil areas due to these advantages. This dissertation focuses on the signal processing of PSA, including signal filtering, signal detection and signal parameter estimation to demonstrate the advantages of PSA over common arrays quantitatively, theoretically. The investigation will enrich the theory of array signal processing and guide the systemic design of PAS. In addition, it will avail to gather and process the information of modem war and provide novel ideas for the bottle-neck in the 3rd generation mobile communication.Signal filtering of PSA means suppressing interference and enhancing the desired signal, using the difference between desired signal and interference in spatial and polanzational domains. At first, the well-known filtering criterions are analyzed comparatively and a novel criterion is brought forth under multiple desired signals case. Then, the filtering performance is investigated under completely polarized case, partially polarized case and correlative case. It is showed that the PSA is more difficult to jam. To produce a poor SINR, the optimal interference signal must both arrive from the same direction and have the same polarization as the desired signal. With equal hardware equipment, PSA obtain the polarizational filtering ability at the cost of losing partial spatial filtering ability. The higher the polarization degree of desired signal being, the better filtering performance is. The higher the polarization degree of interference being, the interference can be suppressed more easily. The correlative coefficient between desired signal and interference affects the filtering performance strongly, which changes the direction of optimal interference. At last, the Double Message Signal is proposed and the non-interfere condition is presented that the steering vectors are orthogonal in the weighted inner product space, with the inverse matrix of background covariance as the weighting matrix. The optimal polarization states are designed, according to the EM background.Signal detection of PSA means detecting the signal at given direction with unknown polarization state under generalized noise background. At first, Matched Subspace Detector (MSD) and Adaptive Subspace Detector (ASD) are derived theoretically. With known noise level, the MSD is of "power type". With unknown noise level, the MSD is of "angle type". All the MSDs possess CFAR property. When the covariance of noise is unknown, the ASD is derived by using Kelly's thought for reference. Then, the signal detection of completely polarized isinvestigated with exploiting the MSD theory. It is shown that the detection probability decreases when interference approaches signal in spatial domain and when the signal direction is misaligned with the look direction. Compared with the common arrays, the detection performance is independent of polarization state of signal, called "Constant Polarization Detection". At last, the signal detection of partially polarized is investigated thoroughly.Signal parameter estimation of PSA means estimating the angles of arrival and the polarization angles simultaneously according to the samples of PSA. Firstly, the joint spectrum is established in polarizational and spatial domains utilizing the MUSIC algorithm. The positions of spectrum peaks indicate the characteristic parameters of EM signals. Secondly, the estimation accuracy and resolving power of joint spectrum are investigated. The Cramer-Rao bound (CRB) is used to evaluate the estimation accuracy, obtained thought inversing the Fisher Information Matrix. The estimation accuracy of...