Quaternion-Valued Adaptive Signal Processing and Its Applications to Adaptive Beamforming and Wind Profile Prediction

Quaternion-valued signal processing has received more and more attentions in the past ten years due to the increasing need to process three or four-dimensional signals, such as colour images, vector-sensor arrays, three-phase power systems, dual-polarisation based wireless communica- tion systems, and wind profile prediction. One key operation involved in the derivation of all kinds of adaptive signal processing algorithms is the gradient operator. Although there are some derivations of this operator in literature with different level of details in the quaternion domain, it is still not fully clear how this operator can be derived in the most general case and how it can be applied to various signal processing problems. In this study, we will give a detailed derivation of the quaternion-valued gradient operator with associated properties and then apply it to different areas. In particular, it will be employed to derive the quaternion-valued LMS (QLMS) algorithm and its sparse versions for adaptive beamforming for vector sensor arrays, and another one is its application to wind profile prediction in combination with the classic computational fluid dynamics (CFD) approach. For the adaptive beamforming problem for vector sensor arrays, we consider the crossed- dipole array and the problem of how to reduce the number of sensors involved in the adap- tive beamforming process, so that reduced system complexity and energy consumption can be achieved, whereas an acceptable performance can still be maintained, which is particularly use- ful for large array systems. The quaternion-valued steering vector model for crossed-dipole arrays will be employed, and a reweighted zero attracting (RZA) QLMS algorithm is then pro- posed by introducing a RZA term to the cost function of the original QLMS algorithm. The RZA term aims to have a closer approximation to the l0 norm so that the number of non-zero valued coefficients can be reduced more effectively in the adaptive beamforming process. For wind profile prediction, it can be considered as a signal processing problem and we can solve it using traditional linear and non-linear prediction techniques, such as the proposed QLMS algorithm and its enhanced frequency-domain multi-channel version. On the other hand,it using traditional linear and non-linear prediction techniques, such as the proposed QLMS algorithm and its enhanced frequency-domain multi-channel version. On the other hand,wind flow analysis is also a classical problem in the CFD field, which employs various simulation methods and models to calculate the speed of wind flow at different time. It is accurate but time-consuming with high computational cost. To tackle the problem, a combined approach based on synergies between the statistical signal processing approach and the CFD approach is proposed. There are different ways of combining the signal processing approach and the CFD approach to obtain a more effective and efficient method for wind profile prediction. In the combined method, the signal processing part employs the QLMS algorithm, while for the CFD part, large eddy simulation (LES) based on the Smagorinsky subgrid-scale (SGS) model will be employed so that more efficient wind profile prediction can be achieved