Direction of arrival estimation for spinning antenna based electronic intelligence systems - Alibrahim, Fuad (Department of Electrical Engineering, 2022)
The spinning directional antenna is the most cost-effective antenna configuration for providing high gain over a wide (multi-octave) radio frequency (RF) range. Thus, it is the appropriate antenna configuration for increasing the intercept range of an electronic intelligence (ELINT) system, which is an important requirement due to the advanced capabilities of modern RF emitters. An ELINT system employing the spinning antenna configuration is capable of accurately estimating the direction of arrival (DOA) of the received signals, provided that: the antenna's beamwidth is narrow; the received data is fully spread across the antenna mainlobe; the number of data samples is large; the signal to disturbance ratio (SDR) is large. However, the antenna's beamwidth may be wide due to the wide operating RF range and physical constraints; the received data may be partially spread across the antenna mainlobe due to the agility of the received signals, errors in estimating the signals' parameters and missing data samples; the number of data samples may be small; the SDR may be small. The presence of the aforementioned factors significantly degrades the DOA estimation accuracy. To overcome this problem, we propose the use of biased estimators for estimating the DOA of emitters' signals received by a spinning antenna based ELINT system. The proposed biased DOA estimators were constructed using Bayesian estimation techniques and by performing a linear transformation and an affine transformation on the maximum likelihood (ML) estimator. Using Monte Carlo simulation and real radar data, we demonstrate that: the proposed biased DOA estimators outperform the mean square error (MSE) limit specified by the popular performance benchmark, the Cramer Rao lower bound (CRLB); the proposed Bayesian DOA estimators are capable of improving the DOA estimation accuracy by merging the information contained in several snapshots (antenna scans). This thus supports the hypothesis that biased DOA estimators are capable of accurately estimating the DOA of emitters' signals received by a spinning antenna based ELINT system. Improving the spatial resolution of a spinning antenna based system beyond the Rayleigh resolution limit is a research area that was receiving noticeable interest while we were conducting this research. We propose a user parameter-free super-resolution DOA estimator that overcomes the shortcomings of existing super-resolution DOA estimators. This proposed super-resolution DOA estimator was constructed using the sparse Bayesian learning (SBL) technique. Using Monte Carlo simulation, we demonstrate that the proposed super-resolution DOA estimator outperforms existing super-resolution DOA estimators. This therefore supports the hypothesis that the SBL DOA estimator is capable of reliably resolving signals received by a spinning antenna based system.