A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Accurate measurement of Cn2 profile with Shack-Hartmann data

The precise reconstruction of the turbulent volume is a key point in the development of new-generation Adaptive Optics systems. We propose a new Cn2 profilometry method named CO-SLIDAR (COupled Slope and scIntillation Detection And Ranging), that uses correlations of slopes and scintillation indexes recorded on a Shack-Hartmann from two separated stars. CO-SLIDAR leads to an accurate Cn2 retrieval for both low and high altitude layers. Here, we present an end-to-end simulation of the Cn2 profile measurement. Two Shack-Hartmann geometries are considered. The detection noises are taken into account and a method to subtract the bias is proposed. Results are compared to Cn2 profiles obtained from correlations of slopes only or correlations of scintillation indexes only.Comment: 10 pages, 8 figures, SPIE Conference "Astronomical Telescopes and Instrumentation" 2012, Amsterdam, paper 8447-19