247 research outputs found

    Terahertz Micro-Doppler Radar for Detection and Characterization of Multicopters

    Get PDF
    abstract: The micromotions (e.g. vibration, rotation, etc.,) of a target induce time-varying frequency modulations on the reflected signal, called the micro-Doppler modulations. Micro-Doppler modulations are target specific and may contain information needed to detect and characterize the target. Thus, unlike conventional Doppler radars, Fourier transform cannot be used for the analysis of these time dependent frequency modulations. While Doppler radars can detect the presence of a target and deduce if it is approaching or receding from the radar location, they cannot identify the target. Meaning, for a Doppler radar, a small commercial aircraft and a fighter plane when gliding at the same velocity exhibit similar radar signature. However, using a micro-Doppler radar, the time dependent frequency variations caused by the vibrational and rotational micromotions of the two aircrafts can be captured and analyzed to discern between them. Similarly, micro-Doppler signature can be used to distinguish a multicopter from a bird, a quadcopter from a hexacopter or a octacopter, a bus from a car or a truck and even one person from another. In all these scenarios, joint time-frequency transforms must be employed for the analysis of micro-Doppler variations, in order to extract the targets’ features. Due to ample bandwidth, THz radiation provides richer radar signals than the microwave systems. Thus, a Terahertz (THz) micro-Doppler radar is developed in this work for the detection and characterization of the micro-Doppler signatures of quadcopters. The radar is implemented as a continuous-wave (CW) radar in monostatic configuration and operates at a low-THz frequency of 270 GHz. A linear time-frequency transform, the short-time Fourier transform (STFT) is used for the analysis the micro-Doppler signature. The designed radar has been built and measurements are carried out using a quadcopter to detect the micro-Doppler modulations caused by the rotation of its propellers. The spectrograms are obtained for a quadcopter hovering in front of the radar and analysis methods are developed for characterizing the frequency variations caused by the rotational and vibrational micromotions of the quadcopter. The proposed method can be effective for distinguishing the quadcopters from other flying targets like birds which lack the rotational micromotions.Dissertation/ThesisMasters Thesis Electrical Engineering 201

    Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022)

    Get PDF
    Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m similar to 2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate

    A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

    Full text link
    The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

    Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

    Get PDF
    In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation

    Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges

    Get PDF
    Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

    A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

    Get PDF
    In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-The-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner alphaalpha -etaeta filter, features extraction, and finally classification of targets by means of a kk-nearest neighbor ( kk-NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks)

    Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

    Get PDF
    Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

    Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring

    Get PDF
    Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems. This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar. The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities

    Remote Sensing

    Get PDF
    This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

    Indoor human activity recognition using high-dimensional sensors and deep neural networks

    Get PDF
    Many smart home applications rely on indoor human activity recognition. This challenge is currently primarily tackled by employing video camera sensors. However, the use of such sensors is characterized by fundamental technical deficiencies in an indoor environment, often also resulting in a breach of privacy. In contrast, a radar sensor resolves most of these flaws and maintains privacy in particular. In this paper, we investigate a novel approach toward automatic indoor human activity recognition, feeding high-dimensional radar and video camera sensor data into several deep neural networks. Furthermore, we explore the efficacy of sensor fusion to provide a solution in less than ideal circumstances. We validate our approach on two newly constructed and published data sets that consist of 2347 and 1505 samples distributed over six different types of gestures and events, respectively. From our analysis, we can conclude that, when considering a radar sensor, it is optimal to make use of a three-dimensional convolutional neural network that takes as input sequential range-Doppler maps. This model achieves 12.22% and 2.97% error rate on the gestures and the events data set, respectively. A pretrained residual network is employed to deal with the video camera sensor data and obtains 1.67% and 3.00% error rate on the same data sets. We show that there exists a clear benefit in combining both sensors to enable activity recognition in the case of less than ideal circumstances
    • …
    corecore