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Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

As part of Local Conversion and Implementation Plan which is based on the EUROCONTROL Revised Convention the Republic of Croatia has undertaken to make a plan of implementing the Basic Continuous

Receiver Autonomous Integrity Monitoring (RAIM) is a method, used by an aircraft's receiver, for detecting and isolating faulty satellites of the Global Navigation Satellite System (GNSS). In order for a receiver to be able to detect and isolate a faulty satellite using a RAIM algorithm, a couple of conditions must be met: a minimum number of satellites, and an adequate satellite geometry. Due to the highly predictable orbits of the GPS satellites, a RAIM availability prediction can be done easily. A number of RAIM methods exist; however, none of them takes into account the precise terrain masking of the satellites for the specific location. They consider a uniform fixed mask angle over the whole horizon. This paper will introduce the variable mask RAIM algorithm in order to show to what extent the terrain can affect the RAIM availability and how much it differs from the conventional algorithms. K E Y

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