Maciej Wielgo

Bio: Maciej Wielgo is an academic researcher from Warsaw University of Technology. The author has contributed to research in topics: Radar imaging & Synthetic aperture radar. The author has an hindex of 7, co-authored 26 publications receiving 176 citations.

35 GHz FMCW drone detection system

Abstract: This paper presents an experimental system dedicated for the detection and tracking of small aerial targets such as Unmanned Aerial Vehicles (UAVs) in particular small drones (multirotors). The system was proposed in response to increasing drone popularity and the related threats. The hardware and software parts of the system are covered, including the analogue front-end, FPGA based pre-processing and PC based processing, detection and tracking. Real life trials are described with promising results provided. Further research on the subject is proposed.

SARENKA - C-band SAR radar for UAV application

Abstract: This paper presents the hardware design, signal processing aspects and first trial results of a C-band Synthetic Aperture Radar (SAR) named SARENKA developed at the Warsaw University of Technology, Poland. The main goal of the presented work was to design a low-cost, small-size and low-weight C-band SAR radar dedicated for medium class unmanned aerial vehicles (UAV) and small aircraft. The developed system allows high resolution SAR images (up to 10cm) of an observed ground area in almost all weather conditions to be obtained. The high resolution in the range direction was obtained using the Frequency Modulation Continuous Wave (FMCW) mode. The high resolution in the cross-range was obtained using advanced SAR signal processing and autofocusing methods, which allow for good azimuth compression using low cost INS/GPS systems dedicated mostly for cheap amateur UAV platforms. The system was preliminary tested using a private car as a ground moving platform. The next step of the system tests will be trials with an ultralight aircraft.

Micro-range, micro-Doppler joint analysis of pedestrian radar echo

Abstract: We present in the paper results of a generalised micro-Doppler analysis that is suited to high range resolution radar. The target echo energy is here represented in the three dimensional space of time, Doppler, and additionally range, what can provide more information on target micro-motion than the standard micro-Doppler analysis which is performed in the time-Doppler plane. The analyzed signals were recorded with a Ka-band FMCW radar of a high 1GHz bandwidth, which could provide good resolution in range, time and velocity. Here, we present a new method to visualize jointly micro-range and micro-doppler effects of a running man observed on the background of ground interferences.

Doppler-only tracking in GSM-based passive radar

Abstract: A passive radar is a radar which uses external noncooperating transmitters (e.g. FM radio or GSM) to illuminate the target. Typical passive radar receiver benefits from its long integration time with a good velocity (Doppler frequency) estimation accuracy, while - especially for a GSM-based radar - the range measurement accuracy may be poor. This paper presents a study of Doppler-only tracking idea, where the strength of a passive radar velocity measurement is exploited. Moreover, Doppler-only localization is also presented. Tracking system simulations are shown as a proof-of-concept, where a significant improvement of tracking accuracy was achieved.

Multistatic passive radar based on WIFI - Results of the experiment

Abstract: In this paper results of an experiment using a multistatic passive radar system based on packet wireless network illuminators are presented. The network consists of several transmitters/receivers that operate on the same channel and exchange data with each other. The passive radar listens to the communication and performs decoding of captured frames, and determines the WiFi network node which was the source of the transmitted frame. The received data stream is divided, based upon the decoded source address, into data streams sent by each network node, after which the separated data streams are processed using classical Passive Coherent Localization methods in order to detect target presence and to localize detected targets. The paper presents details of the experiments performed and their results.

Detection, Tracking, and Interdiction for Amateur Drones

Abstract: Unmanned aerial vehicles, also known as drones, are expected to play major roles in future smart cities, for example, by delivering goods and merchandise, serving as mobile hotspots for broadband wireless access, and maintaining surveillance and security. The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones. In particular, we review techniques that rely on ambient radio frequency signals (emitted from UAVs), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs. We present some early experimental and simulation results on range estimation of UAVs and receding horizon tracking of UAVs. Finally, we summarize common techniques that are considered for interdiction of UAVs.

Machine Learning Inspired Sound-Based Amateur Drone Detection for Public Safety Applications

Abstract: In recent years, popularity of unmanned air vehicles enormously increased due to their autonomous moving capability and applications in various domains. This also results in some serious security threats, that needs proper investigation and timely detection of the amateur drones (ADr) to protect the security sensitive institutions. In this paper, we propose the novel machine learning (ML) framework for detection and classification of ADr sounds out of the various sounds like bird, airplanes, and thunderstorm in the noisy environment. To extract the necessary features from ADr sound, Mel frequency cepstral coefficients (MFCC), and linear predictive cepstral coefficients (LPCC) feature extraction techniques are implemented. After feature extraction, support vector machines (SVM) with various kernels are adopted to accurately classify these sounds. The experimental results verify that SVM cubic kernel with MFCC outperform LPCC method by achieving around 96.7% accuracy for ADr detection. Moreover, the results verified that the proposed ML scheme has more than 17% detection accuracy, compared with correlation-based drone sound detection scheme that ignores ML prediction.

Tutorial: Passive radar tutorial

Abstract: Passive Radar signifying the localisation of a target by radar measurements without using own controlled emissions has been discussed, tried, reinvented, and matured within the last 80 years. Its advantages, like covert operation and saving the costs of a transmitter, are obvious. Military as well as civilian interests combined with the advances in technological developments have recently boosted research on passive radar and passive radar systems are currently approaching the market. This tutorial shall give an overview of the history, development, and processing in passive radar and enable the interested reader to further investigate the subject exploiting the presented material together with the cited references.

35 GHz FMCW drone detection system

Abstract: This paper presents an experimental system dedicated for the detection and tracking of small aerial targets such as Unmanned Aerial Vehicles (UAVs) in particular small drones (multirotors). The system was proposed in response to increasing drone popularity and the related threats. The hardware and software parts of the system are covered, including the analogue front-end, FPGA based pre-processing and PC based processing, detection and tracking. Real life trials are described with promising results provided. Further research on the subject is proposed.

Blind Detection: Advanced Techniques for WiFi-Based Drone Surveillance

Abstract: The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper, we propose a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real-life scenarios, with a peak true positive rate of ${\text{96}}$ %.