Antenna array calibration in wireless communications

Imperial Users onl

Subspace methods for self-calibration of ULAs with unknown mutual coupling: A false-peak analysis

Self-calibration can mitigate the adverse effect of array imperfections in direction-of-arrival (DoA) estimation. In this paper, we consider self-calibration of uniform linear arrays (ULAs) with unknown mutual coupling and focus on subspace-based methods. By modeling the effect of mutual coupling through complex symmetric Toeplitz matrices, we first analyze its influence to existing subspace methods. Our analytical results illustrate that mutual coupling can induce false peaks in spatial spectra of these methods, and more importantly, most of these peaks are predictable. Based on the analytical results, we then propose new spectra to suppress these predictable false peaks, and new self-calibration algorithms for the proposed spectra. Simulations are performed to validate our analytical results and to illustrate the advantages of the proposed algorithms

A Tutorial on Time-Evolving Dynamical Bayesian Inference

In view of the current availability and variety of measured data, there is an increasing demand for powerful signal processing tools that can cope successfully with the associated problems that often arise when data are being analysed. In practice many of the data-generating systems are not only time-variable, but also influenced by neighbouring systems and subject to random fluctuations (noise) from their environments. To encompass problems of this kind, we present a tutorial about the dynamical Bayesian inference of time-evolving coupled systems in the presence of noise. It includes the necessary theoretical description and the algorithms for its implementation. For general programming purposes, a pseudocode description is also given. Examples based on coupled phase and limit-cycle oscillators illustrate the salient features of phase dynamics inference. State domain inference is illustrated with an example of coupled chaotic oscillators. The applicability of the latter example to secure communications based on the modulation of coupling functions is outlined. MatLab codes for implementation of the method, as well as for the explicit examples, accompany the tutorial.Comment: Matlab codes can be found on http://py-biomedical.lancaster.ac.uk

Analytical evaluation of uncertainty on active antenna arrays

An analytical method for evaluating the uncertainty of the performance of active antenna arrays in the whole spatial spectrum is presented. Since array processing algorithms based on spatial reference are widely used to track moving targets, it is essential to be aware of the impact of the uncertainty sources on the antenna response. Furthermore, the estimation of the direction of arrival (DOA) depends on the array uncertainty. The aim of the uncertainties analysis is to provide an exhaustive characterization of the behavior of the active antenna array associated with its main uncertainty sources. The result of this analysis helps to select the proper calibration technique to be implemented. An illustrative example for a triangular antenna array used for satellite tracking is presented showing the suitability of the proposed method to carry out an efficient characterization of an active antenna array

Speech rhythms and multiplexed oscillatory sensory coding in the human brain

Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations

Enhanced Direction of Arrival Estimation through Electromagnetic Modeling

Engineering is a high art that balances modeling the physical world and designing meaningful solutions based on those models. Array signal processing is no exception, and many innovative and creative solutions have come from the field of array processing. However, many of the innovative algorithms that permeate the field are based on a very simple signal model of an array. This simple, although powerful, model is at times a pale reflection of the complexities inherent in the physical world, and this model mismatch opens the door to the performance degradation of any solution for which the model underpins. This dissertation seeks to explore the impact of model mismatch upon common array processing algorithms. To that end, this dissertation brings together the disparate topics of electromagnetics and signal processing. Electromagnetics brings a singular focus on the physical interactions of electromagnetic waves and physical array structures, while signal processing brings modern computational power to solve difficult problems. We delve into model mismatch in two ways; first, by developing a blind array calibration routine that estimates model mismatch and incorporates that knowledge into the reiterative superresoluiton (RISR) direction of arrival estimation algorithm; second, by examining model mismatch between a transmitting and receiving array, and assessing the impact of this mismatch on prolific direction of arrival estimation algorithms. In both of these studies we show that engineers have traded algorithm performance for model simplicity, and that if we are willing to deal with the added complexity we can recapture that lost performance