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Abstract-The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector.The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a differential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the efficacy of the proposed algorithm.

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Abstract-The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch between the actual and presumed steering vectors can be mainly due to the error in the look direction estimate. In this paper, we propose a novel algorithm to estimate the look direction and to reconstruct the covariance matrix so that near optimal performance without the effect of saturation can be achieved as the input SNR increases. Numerical results also show that all existing beamforming algorithms suffer from saturation effect as the input SNR increases.

The ionospheric total electron content (TEC) in the low-latitude Singapore region (geographic latitude 01.37°N, longitude, 103.67°E, geomagnetic latitude 8.5°S) for 2010 to 2011 was retrieved using the data from global positioning system (GPS)-based measurements. The observed TEC from GPS is compared with those derived from the latest International Reference Ionosphere (IRI)-2012 model with three options, IRI-Nequick (IRI-Neq), IRI-2001, and IRI-01-Corr, for topside electron density. The results showed that the IRI-Neq and IRI-01-Corr models are in good agreement with GPS-TEC values at all times, in all seasons, for the year 2010. For the year 2011, these two models showed agreement at all times with GPS-TEC only for the summer season, and for the period 11:00 to 24:00 UT hours (19:00 to 24:00 LT and 00:00 to 08:00 LT) during the winter and equinox seasons. The IRI-2012 model electron density profile showed agreement with constellation observing system for meteorology, ionosphere, and climate (COSMIC) radio occultation (RO)-based measurements around 250 to 300 km and was found to be independent of the options for topside density profiles. However, above 300 km, the IRI-2012 model electron density profile does not show agreement with COSMIC measurements. The observations (COSMIC and GPS) and IRI-2012-based data of TEC and electron density profiles were also analyzed during quiet and storm periods. The analysis showed that the IRI model does not represent the impact of storms, while observations show the impact of storms on the low-latitude ionosphere. This suggests that significant improvements in the IRI model are required for estimating behavior during storms, particularly in low-latitude regions.