This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper’s novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time–frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time–frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner–Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from −6 to 0 dB.

This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (IST-106, IST-132 and IST-165) which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction between devices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds’ activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study

This paper describes a concept for the construction and application of the Counter-UAV Defence System as developed by the Polish consortium of Zakłady Mechaniczne “Tarnów” S.A. (ZMT) and Military University of Technology (MUT). The article describes a system designed to counteract unmanned aerial vehicles, which consists of a multi-barrel machine gun and a radiolocation system. The Counter-UAV Defence System enables the combating of aerial targets, including unmanned aerial vehicles (UAVs).

In Information Fusion, like in other branches of The Applied Informatics there is an observable interest of the researchers in finding solutions of the known problems, exploring the potential of the existing approaches, and application of new techniques. The aim of this paper is to provide a concept of application of the new techniques: Polymorphic Models in Swarm-based Recognition System in order to resolve efficiently the well-known problem of Threat Assessment.