GLRT-based threshold detection-estimation performance improvement and application to uniform circular antenna arrays

©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."The problem of estimating the number of independent Gaussian sources and their parameters impinging upon an antenna array is addressed for scenarios that are problematic for standard techniques, namely, under "threshold conditions" (where subspace techniques such as MUSIC experience an abrupt and dramatic performance breakdown). We propose an antenna geometry-invariant method that adopts the generalized-likelihood-ratio test (GLRT) methodology, supported by a maximum-likelihood-ratio lower-bound analysis that allows erroneous solutions ("outliers") to be found and rectified. Detection-estimation performance in both uniform circular and linear antenna arrays is shown to be significantly improved compared with conventional techniques but limited by the performance-breakdown phenomenon that is intrinsic to all such maximum-likelihood (ML) techniques.Yuri I. Abramovich, Nicholas K. Spencer, and Alexei Y. Gorokho

Secret Key Generation Based on AoA Estimation for Low SNR Conditions

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR

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