Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

Techniques for automatic modulation classification (AMC) of radar signals are crucial for spectrum sensing, passive bistatic radars and electronic intelligence (ELINT). Most of existing AMC algorithms were evaluated using solely synthetic data. Meanwhile, signals intercepted in real-life scenarios are extra distorted due to the multipath propagation. This paper presents the novel, pattern recognition AMC method for frequency modulated radar signals, with improved resistance to noise and multipath. This has been achieved by a multi-stage feature selection process, involving over a dozen of popular radar signal metrics. Based on the feature selection results, the two advanced waveform features were utilized in the designed algorithm, i.e. quasi-maximum likelihood (QML) instantaneous frequency estimate and fractional Fourier transform (FrFT) profile. The proposed AMC method has been evaluated using both synthetic and real data. Obtained results show that proposed classification framework achieves overall accuracy of 93.6% for a set of real-life signals acquisitions, corrupted both by noise and multipath influence.

Synthetic aperture radar (SAR) imaging systems are becoming increasingly useful in military and civilian surveillance applications. Installation of a SAR system on a miniature unmanned aerial vehicle (mini UAV) enables high resolution terrain imaging in adverse observational conditions, e.g. at night, in fog or in smoke, where classical or infrared cameras would be useless. Use of a small and relatively low-cost radar platform allows for reduction of the mission costs and reduces the risk of detection and destruction of the carrier by the enemy's air defence. The paper presents a concept, a structure, and the most important elements of the UAV-based SAR terrain imaging system, currently developed in a project WATSAR, jointly realized by the Institute of Radioelectronics of the Faculty of Electronics, Military University of Technology, Warsaw, Poland and a Polish private company WB Electronics S.A., under a grant financed by the National Centre for Research and Development. It is assumed that the system will provide high resolution radar images, comparable with aerial photographs, which will be used for tactical scale military reconnaissance. The SAR images from the developed system can also augment multisensory data processed in Battlefield Management Systems (BMS), supporting decision processes on contemporary battlefield and increasing situational awareness of individual soldiers.

The paper describes a Synthetic Aperture Radar system currently developed by the Military University of Technology and a Polish private company WB Electronics S.A. in a frame of a project under acronym WATSAR. The project is aimed at designing, development and testing of a terrain imaging radar system operating from a miniature UAV. The paper presents structure and way of operation of the system and its current state of development. The main focus of the paper is directed onto presentation of radar images obtained during two flight campaigns realized in May and July 2015. The paper addresses also the algorithms used for SAR processing and the adopted methodology of in-flight experiments.