GNSS based passive radar for UAV monitoring

Monitoring of unmanned aerial vehicle (UAV) targets has been a subject of great importance in both defence and security sectors. In this paper a novel system is introduced based on a passive bistatic radar using Global Navigation Satellite Systems (GNSS) as illuminators of opportunity. Particularly, a link budget analysis is held to determine the capabilities and limitations of such a system. Additionally, a signal reconstruction algorithm is provided allowing estimation of the transmitted signal from each satellite. Finally, the proposed system is tested in outdoor acquisitions of small UAV targets where the Fractional Fourier Transform (FrFT) is used as tool to enhance target detectability

Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles

The micro-Doppler signature is a time-varying frequency modulation imparted on radar echo caused by target’s micro-motion. To save the trouble of constructing template in the target classification, this paper investigates the micro-Doppler signature of wheeled and tracked vehicles and proposes a template-free classification method. Firstly, the echo signature is established and the micro-Doppler difference of these two kinds of targets is analysed. Secondly, some new micro-Doppler features are defined according to their difference. The new defined features are micro-Doppler bandwidth, micro-Doppler expansion rate and micro-Doppler peak number. According to the characteristic of the micro-Doppler in the time-frequency domain, we proposed to realise the feature extraction by Hough transformation. Lastly, template-free subjection functions are proposed to define the relationship between the features and the vehicles. By fuzzy comprehensive evaluation, the final classification result is obtained by combining the subjection probabilities together. Experimental results based on the simulated data and measured data are presented, which prove that the algorithm has good performance

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.Comment: This paper has been submitted to IEEE Radar Conference 201

GNSS based passive UAV monitoring : feasibility study

As unmanned aerial vehicles (UAVs) are becoming more accessible and easier to use, the need for a reliable and easy to deploy monitoring solution has become the subject of great importance in defence, security and commercial sectors. In this work, a novel passive bistatic radar is proposed to facilitate UAV detection and localisation by exploiting Global Navigation Satellite Systems (GNSS) as illuminators of opportunity (IO). The result of a feasibility study conducted to determine the maximum operational range of the system under different configurations as well as its parameter estimation capabilities were evaluated through simulations. To facilitate multiple satellites signals, a multiple-input single-output (MISO) approach is adapted to estimate the target’s location and velocity

Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems

Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems. Two Synthetic Aperture Radar (SAR) ATR algorithms are described for full- and single-polarimetric images, and tested on the GOTCHA and the MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments, that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to the availability of several images from multiple sensors through the implementation of a simple fusion rule. A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating the number, the length and the rotation speed of the blades, parameters that are peculiar for each helicopter’s model. The algorithm is extended to deal with the identification of multiple helicopters flying in formation that cannot be resolved in another domain. Moreover, a fusion rule is presented to integrate the results of the identification performed from several sensors in a distributed radar system. Tests performed both on simulated signals and on real signals acquired from a scale model of a helicopter, confirm the validity of the algorithm. Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp sub-carriers generated through the Fractional Fourier Transform (FrFT), with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based waveform is extensively tested and compared with Orthogonal Frequency Division Multiplexing (OFDM) and LFM waveforms, in order to assess both its radar and communication performance

Програмно-апаратний комплекс для оцінки характеристик кровотоку

Структура та обсяг роботи. Магістерська дисертація складається зі вступу, 3 розділів, висновку, переліку посилань, 1 додатку. Повний обсяг роботи становить 89 сторінок, міститься 39 ілюстрацій, 27 таблиць. Загалом опрацьовано 46 джерел. Актуальність теми роботи. Дослідження кровообігу людини має важливе значення для діагностики та прогнозування патологічних станів у кардіології. Але динамічний характер досліджуванної системи, труднощі врахування всіх її особливостей, сторонні впливи, обмежені можливості техніки ускладнюють діагностику. Тому актуальним є удосконалення систем детектування, розшифровки і обробки сигналів діагностичних приладів, що дасть змогу оптимізуючи існуючі в медичних закладах, а також в закладах освіти, засоби, виявити особливості центральної та регіонарної гемодинаміки і здійснити прийняття обгрунтованих рішень в кардіології. Мета роботи. Розробка програмно-апаратного комплексу для оцінки характеру кровотоку людини на основі аналогового ультразвукового допплерівського приладу. Задачі: – опрацювати літературні джерела щодо особливостей дослідження кровотоку та існуючих методів обробки сигналів; – обрати метод детектування і обробки сигналу приладу; – реалізувати метод засобами електронно-обчислювальної техніки; – реалізувати спряження між приладом і програмним забезпеченням; – розробити віртуальну модель сигналу та обробити отримані дані; – розробити стартап-проект. Об’єкт дослідження. Засоби дослідження кровотоку людини. Предмет дослідження. Ультразвуковий допплерівський вимірювач швидкості кровотоку. Методи дослідження. Ультразвукова допплерографія, методи спекрального аналізу, під час виконання розрахунків, отримання зображень та розробки коду було використано програмне забезпечення MicroCap 12, DipTrace, NI LabVIEW 2020, PcLab2000LT. Наукова новизна одержаних результатів. Створений програмно-апаратний комплекс надає необхідний інструментарій для отримання, обробки та аналізування даних кровотоку з гнучким налаштуванням та можливістю адаптації програмного забезпечення під різні засоби детектування. Практичне значення одержаних результатів. Створений програмно-апаратний комплекс для оцінки характеристик кровотоку людини може бути використаний в подальшому для проведеня досліджень на кафедрі біомедичної інженерії та у навчальному процесі загалом для дослідження кровотоку людини та принципів функціонування допплерівських приладів. Апробація результатів дисертації. результати роботи були оприлюднені на Міжнародній науково-практичній конференції «Інформаційні системи та технології в медицині» ISM-2020. Магістерська робота виконувалась згідно напрямку наукової діяльності кафедри – науково-дослідницької роботи «Лабораторно-діагностичний комплекс для дослідження біофізичних параметрів і функціональних змін організму людини», державний реєстраційний номер 0119U103861.Structure and scope of work. The work consists of an introduction, 3 sections, a conclusion, a list of references, one appendix. The total volume of the work is 89 pages, contains 39 illustrations, 27 tables. In total 46 sources were processed. Relevance of the topic. The study of human blood circulation is important for the diagnosis and prediction of pathological conditions in cardiology. But the dynamic nature of the studied system, the difficulty of taking into account all its features, external influences, limited capabilities of technology complicate the diagnosis. Therefore, it is important to improve the systems of detection, decoding and signal processing of diagnostic devices, which will optimize existing in medical institutions, as well as in educational institutions, tools to identify features of central and regional hemodynamics and make informed decisions in cardiology. The purpose of the work. Development of software and hardware complex for assessing the nature of human blood flow based on analog ultrasound Doppler device. Tasks: – to study the literature on the features of the study of blood flow and existing methods of signal processing; – choose the method of detection and signal processing of the device; – implement the method by means of electronic computers; – implement the connection between the device and the software; – develop a virtual signal model and process the received data; – develop a startup project. Object of study. Means of studying human blood flow. Subject of study. Ultrasonic Doppler blood flow meter. Research methods. Ultrasound Doppler, spectral analysis methods, during the calculations, image acquisition and code development were used software MicroCap 12, DipTrace, NI LabVIEW 2020, PcLab2000LT. Scientific novelty of the obtained results. The created software and hardware complex provides the necessary tools for obtaining, processing and analyzing blood flow data with flexible settings and the ability to adapt the software to different detection tools. The practical significance of the obtained results. The created software and hardware complex for assessing the characteristics of human blood flow can be used in the future for research at the Department of Biomedical Engineering and in the educational process in general for the study of human blood flow and the principles of Doppler devices. Approbation of results. The results were announced at the International Scientific and Practical Conference "Information Systems and Technologies in Medicine" ISM-2020. The master's thesis was performed according to the direction of scientific activity of the department - research work "Laboratory-diagnostic complex for the study of biophysical parameters and functional changes of the human body", state registration number 0119U103861

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT’s performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition