165 research outputs found

    Development and Evaluation of a Multistatic Ultrawideband Random Noise Radar

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    This research studies the AFIT noise network (NoNET) radar node design and the feasibility in processing the bistatic channel information of a cluster of widely distributed noise radar nodes. A system characterization is used to predict theoretical localization performance metrics. Design and integration of a distributed and central signal and data processing architecture enables the Matlab®-driven signal data acquisition, digital processing and multi-sensor image fusion. Experimental evaluation of the monostatic localization performance reveals its range measurement error standard deviation is 4.8 cm with a range resolution of 87.2(±5.9) cm. The 16-channel multistatic solution results in a 2-dimensional localization error of 7.7(±3.1) cm and a comparative analysis is performed against the netted monostatic solution. Results show that active sensing with a low probability of intercept (LPI) multistatic radar, like the NoNET, is capable of producing sub-meter accuracy and near meter-resolution imagery

    A low-cost through-the-wall FMCW radar for stand-off operation and activity detection

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    In this paper we present a new through-wall (TW) FMCW radar system. The architecture of the radar enables both high sensitivity and range resolutions of <1.5 m. Moreover, the radar employs moving target indication (MTI) signal processing to remove the problematic primary wall reflection, allowing higher signal-to- noise and signal-to-interference ratios, which can be traded-off for increased operational stand-off. The TW radar operates at 5.8 GHz with a 200 MHz bandwidth. Its dual-frequency design minimises interference from signal leakage, and permits a baseband output after deramping which is digitized using an inexpensive 24-bit off-the-shelf sound card. The system is therefore an order of magnitude lower in cost than competitor ultrawideband (UWB) TW systems. The high sensitivity afforded by this wide dynamic range has allowed us to develop a wall removal technique whereby high-order digital filters provide a flexible means of MTI filtering based on the phases of the returned echoes. Experimental data demonstrates through-wall detection of individuals and groups of people in various scenarios. Target positions were located to within ±1.25 m in range, allowing us distinguish between two closely separated targets. Furthermore, at 8.5 m standoff, our wall removal technique can recover target responses that would have otherwise been masked by the primary wall reflection, thus increasing the stand-off capability of the radar. Using phase processing, our experimental data also reveals a clear difference in the micro-Doppler signatures across various types of everyday actions

    Multiple moving target detection with ultra wideband radar using super-resolution algorithms

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    The improvements in microwave electronics opened the way to build microwave components such as low noise amplifiers, samplers and pulse generators that are broadband. As these building blocks are being developed, new applications become subject of research. Ultra wideband radar is one of these subjects. Major applications of ultra wideband radars are behind the wall imaging, biomedical imaging and buried land mine detection. In this study we aimed to locate multiple scatterers that are moving. Even though there are many scatterers in an environment, detection of moving targets is possible using differences of successive radar snapshots. This is generally the case when behind the wall human targets are to be detected. We investigated the effectiveness of various types Multiple Signal Classification (MUSIC) algorithms on the data acquired by our ultra wideband radar prototype. In ideal computer simulations, Time Reversal MUSIC (TRM) algorithm provides successful estimations of both directions and distances of multiple targets. However in practice where non-ideal effects are existent, the performance of TRM algorithm is estimating the target distances degrades. On the other hand, Delay Estimation MUSIC algorithm provides better estimates for the distances of the targets since it is less sensitive to phase noise. Combining the output of TRM algorithm for target directions and the output of Delay Estimation MUSIC method for target distances resulted in successful localization of targets. Experiments are performed using two moving targets in order to test the effectiveness the proposed processing scheme. The problem of detection ambiguities is also considered and several methods to resolve actual targets are presented

    Development and performance evaluation of a multistatic radar system

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    Multistatic radar systems are of emerging interest as they can exploit spatial diversity, enabling improved performance and new applications. Their development is being fuelled by advances in enabling technologies in such fields as communications and Digital Signal Processing (DSP). Such systems differ from typical modern active radar systems through consisting of multiple spatially diverse transmitter and receiver sites. Due to this spatial diversity, these systems present challenges in managing their operation as well as in usefully combining the multiple sources of information to give an output to the radar operator. In this work, a novel digital Commercial Off-The-Shelf (COTS) based coherent multistatic radar system designed at University College London, named ‘NetRad’, has been developed to produce some of the first published experimental results, investigating the challenges of operating such a system, and determining what level of performance might be achievable. Full detail of the various stages involved in the combination of data from the component transmitter-receiver pairs within a multistatic system is investigated, and many of the practical issues inherent are discussed. Simulation and subsequent experimental verification of several centralised and decentralised detection algorithms in terms of localisation (resolution and parameter estimation) of targets was undertaken. The computational cost of the DSP involved in multistatic data fusion is also considered. This gave a clear demonstration of several of the benefits of multistatic radar. Resolution of multiple targets that would have been unresolvable in a conventional monostatic system was shown. Targets were also shown to be plotted as two-dimensional vector position and velocities from use of time delay and Doppler shift information only. A range of targets were used including some such as walking people which were particularly challenging due to the variability of Radar Cross Section (RCS). Performance improvements were found to be dependant on the type of multistatic radar, method of data fusion and target characteristics in question. It is likely that future work will look to further explore the optimisation of multistatic radar for the various measures of performance identified and discussed in this work

    Toward Deep Learning-Based Human Target Analysis

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    In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration

    A comparison of processing approaches for distributed radar sensing

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    Radar networks received increasing attention in recent years as they can outperform single monostatic or bistatic systems. Further attention is being dedicated to these systems as an application of the MIMO concept, well know in communications for increasing the capacity of the channel and improving the overall quality of the connection. However, it is here shown that radar network can take advantage not only from the angular diversity in observing the target, but also from a variety of ways of processing the received signals. The number of devices comprising the network has also been taken into the analysis. Detection and false alarm are evaluated in noise only and clutter from a theoretical and simulated point of view. Particular attention is dedicated to the statistics behind the processing. Experiments have been performed to evaluate practical applications of the proposed processing approaches and to validate assumptions made in the theoretical analysis. In particular, the radar network used for gathering real data is made up of two transmitters and three receivers. More than two transmitters are well known to generate mutual interference and therefore require additional e�fforts to mitigate the system self-interference. However, this allowed studying aspects of multistatic clutter, such as correlation, which represent a first and novel insight in this topic. Moreover, two approaches for localizing targets have been developed. Whilst the first is a graphic approach, the second is hybrid numerical (partially decentralized, partially centralized) which is clearly shown to improve dramatically the single radar accuracy. Finally the e�ects of exchanging angular with frequency diversity are shown as well in some particular cases. This led to develop the Frequency MIMO and the Frequency Diverse Array, according to the separation of two consecutive frequencies. The latter is a brand new topic in technical literature, which is attracting the interest of the technical community because of its potential to generate range-dependant patterns. Both the latter systems can be used in radar-designing to improve the agility and the effciency of the radar

    Multistatic radar optimization for radar sensor network applications

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    The design of radar sensor networks (RSN) has undergone great advancements in recent years. In fact, this kind of system is characterized by a high degree of design flexibility due to the multiplicity of radar nodes and data fusion approaches. This thesis focuses on the development and analysis of RSN architectures to optimize target detection and positioning performances. A special focus is placed upon distributed (statistical) multiple-input multipleoutput (MIMO) RSN systems, where spatial diversity could be leveraged to enhance radar target detection capabilities. In the first part of this thesis, the spatial diversity is leveraged in conjunction with cognitive waveform selection and design techniques to quickly adapt to target scene variations in real time. In the second part, we investigate the impact of RSN geometry, particularly the placement of multistatic radar receivers, on target positioning accuracy. We develop a framework based on cognitive waveform selection in conjunction with adaptive receiver placement strategy to cope with time-varying target scattering characteristics and clutter distribution parameters in the dynamic radar scene. The proposed approach yields better target detection performance and positioning accuracy as compared with conventional methods based on static transmission or stationary multistatic radar topology. The third part of this thesis examines joint radar and communication systems coexistence and operation via two possible architectures. In the first one, several communication nodes in a network operate separately in frequency. Each node leverages the multi-look diversity of the distributed system by activating radar processing on multiple received bistatic streams at each node level in addition to the pre-existing monostatic processing. This architecture is based on the fact that the communication signal, such as the Orthogonal Frequency Division Multiplexing (OFDM) waveform, could be well-suited for radar tasks if the proper waveform parameters are chosen so as to simultaneously perform communication and radar tasks. The advantage of using a joint waveform for both applications is a permanent availability of radar and communication functions via a better use of the occupied spectrum inside the same joint hardware platform. We then examine the second main architecture, which is more complex and deals with separate radar and communication entities with a partial or total spectrum sharing constraint. We investigate the optimum placement of radar receivers for better target positioning accuracy while reducing the radar measurement errors by minimizing the interference caused by simultaneous operation of the communication system. Better performance in terms of communication interference handling and suppression at the radar level, were obtained with the proposed placement approach of radar receivers compared to the geometric dilution of precision (GDOP)-only minimization metric

    Beyond the spatio-temporal limits of atmospheric radars: inverse problem techniques and MIMO systems

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    The Earth’s upper atmosphere (UA) is a highly dynamic region dominated by atmospheric waves and stratified turbulence covering a wide range of spatio-temporal scales. A comprehensive study of the UA requires measurements over a broad range of frequencies and spatial wavelengths, which are prohibitively costly. To improve the understanding of the UA, an investment in efficient and large observational infrastructures is required. This work investigates remote sensing techniques based on MIMO and inverse problems techniques to improve the capabilities of current atmospheric radars

    Wireless Localization Systems: Statistical Modeling and Algorithm Design

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    Wireless localization systems are essential for emerging applications that rely on context-awareness, especially in civil, logistic, and security sectors. Accurate localization in indoor environments is still a challenge and triggers a fervent research activity worldwide. The performance of such systems relies on the quality of range measurements gathered by processing wireless signals within the sensors composing the localization system. Such range estimates serve as observations for the target position inference. The quality of range estimates depends on the network intrinsic properties and signal processing techniques. Therefore, the system design and analysis call for the statistical modeling of range information and the algorithm design for ranging, localization and tracking. The main objectives of this thesis are: (i) the derivation of statistical models and (ii) the design of algorithms for different wire- less localization systems, with particular regard to passive and semi-passive systems (i.e., active radar systems, passive radar systems, and radio frequency identification systems). Statistical models for the range information are derived, low-complexity algorithms with soft-decision and hard-decision are proposed, and several wideband localization systems have been analyzed. The research activity has been conducted also within the framework of different projects in collaboration with companies and other universities, and within a one-year-long research period at Massachusetts Institute of Technology, Cambridge, MA, USA. The analysis of system performance, the derived models, and the proposed algorithms are validated considering different case studies in realistic scenarios and also using the results obtained under the aforementioned projects

    Awireless passive radar system for real-time through-wall movement detection

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    In this paper, a reconfigurable real-time passive wireless detection system is described. The system is based on software-defined radio (SDR) architecture. The signal processing method and processing flow that enable through-wall target detection are introduced. The high-speed noise and interference mitigation methods implemented in the system for through-wall target detection are also described. A series of experimental results are presented for both large and small human body movements in through-wall scenarios. It is shown that the high-resolution Doppler event history implemented in the system enables the system to recognize and distinguish a range of body movements. The results demonstrate that this real-time SDR-based wireless detection system is a low-cost solution for human movement and recognition, with a range of applications
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