The aim of this paper is the methodology of measurements executed in a radio link for the realization of radiolocation services in radiocommunication networks, particularly in cellular networks. The main results of the measurements obtained in the physical layer of the universal mobile telecommunications system (UMTS) are introduced. A new method for the utilization of the multipath propagation phenomenon to improve the estimation of the distance between the mobile station (MS) and the base station (BS) is outlined. This method significantly increases the quality of location services in systems which use a radio interface with direct sequence code division multiple access (DS CDMA).

Mapping storm activity across the globe at time scales ranging from minutes to years is an important element of measuring and forecasting climate change. The WERA system is being used to verify models of the influence of various types of solar activity on the lower layers of the ionosphere. We hope that one day it can also be used on Mars.

To improve the user’s localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application. The user’s position is calculated with a trained neural network system’s models. Additionally the influence of various number of measurements from LTE and WiFi networks in the input vector on the positioning accuracy was examined. From the results it can be seen that using hybrid deep learning algorithm with a radio signatures method can result in localization error 24.3 m and 1.9 m lower comparing respectively to the GPS system and standalone deep learning algorithm with a radio signatures method in indoor environment. What is more, the combination of LTE and WiFi signals measurement in an input vector results in better indoor and outdoor as well as floor classification accuracy and less positioning error comparing to the input vector consisting measurements from only LTE network or from only WiFi network.

In this article the author makes an attempt to characterize the main factor – mobility – which is to be taken into consideration while designing the ground radar component (passive and active radars) of the Air Defense systems in order to enable them to operate on the contemporary battlefield. He presents the latest theoretical views on the relationship between the usage of anti-radiation missiles and the mobility of the passive and active radars as the key ability to protect them against such weapons. In particular the author emphasizes that the antiradiation missiles present nowadays the biggest threat for the effective Air Defense systems, which are today characterized by a high complexity degree. He also stresses the need to combine the radars, both passive and active, into one system allowing for the streamlining of their work parameters and thus ensuring their complex usage. The gaining of those capabilities shall guarantee that the parameters of the air surveillance radar area can be defined effectively.