[PDF] Mimo Radar Waveform Design For Spectrum Sharing With Cellular Systems eBook
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This book discusses spectrum sharing between cellular systems and radars. The book addresses a novel way to design radar waveforms that can enable spectrum sharing between radars and communication systems, without causing interference to communication systems, and at the same time achieving radar objectives of target detection, estimation, and tracking. The book includes a MATLAB-based approach, which provides reader with a way to learn, experiment, compare, and build on top of existing algorithms.
This book presents spectrum sharing efforts between cellular systems and radars. The book addresses coexistence algorithms for radar and communication systems. Topics include radar and cellular system models; spectrum sharing with small radar systems; spectrum sharing with large radar systems; radar spectrum sharing with coordinated multipoint systems (CoMP); and spectrum sharing with overlapped MIMO radars. The primary audience is the radar and wireless communication community, specifically people in industry, academia, and research whose focus is on spectrum sharing. The topics are of interest for both communication and signal processing technical groups. In addition, students can use MATLAB code to enhance their learning experience.
The first book to present a systematic and coherent picture of MIMO radars Due to its potential to improve target detection and discrimination capability, Multiple-Input and Multiple-Output (MIMO) radar has generated significant attention and widespread interest in academia, industry, government labs, and funding agencies. This important new work fills the need for a comprehensive treatment of this emerging field. Edited and authored by leading researchers in the field of MIMO radar research, this book introduces recent developments in the area of MIMO radar to stimulate new concepts, theories, and applications of the topic, and to foster further cross-fertilization of ideas with MIMO communications. Topical coverage includes: Adaptive MIMO radar Beampattern analysis and optimization for MIMO radar MIMO radar for target detection, parameter estimation, tracking,association, and recognition MIMO radar prototypes and measurements Space-time codes for MIMO radar Statistical MIMO radar Waveform design for MIMO radar Written in an easy-to-follow tutorial style, MIMO Radar Signal Processing serves as an excellent course book for graduate students and a valuable reference for researchers in academia and industry.
This comprehensive new resource provides in-depth and timely coverage of the underpinnings and latest advances of MIMO radar. This book provides a comprehensive introduction to MIMO radar and demonstrates it’s utility in real-world applications, then culminates with the latest advances in optimal and adaptive MIMO radar for enhanced detection and target ID in challenging environments. Signal processing prerequisites are explained, including radar signals, orthogonal waveforms, matched filtering, multi-channel beam forming, and Doppler processing. This book discusses MIMO radar signal model, antenna properties, system modeling and waveform alternatives. MIMO implantation challenges are covered, including computational complexity, adaptive clutter mitigation, calibration and equalization, and hardware constraints. Applications for GMTI radar, OTH radar, maritime radar, and automotive radar are explained. The book offers an introduction to optimum MIMO radar and includes details about detection, clutter, and target ID. Insight into adaptive MIMO radar and MIMO channel estimation is presented and techniques and illustrative examples are given. Readers find exclusive flight testing data from DARPA. The breadth of coverage in this all-inclusive resource makes it suitable for both practicing engineers and advanced researchers. The book concludes with discussions on areas for future research.
Recently, multiple-input multiple-output (MIMO) radars have received considerable attention due to their superior resolution. A MIMO radar system lends itself to a networked implementation, which is very desirable in both military and civilian applications. In networked radars, the transmit and receive antennas are placed on wireless connected nodes, such as vehicles, ships, airplanes, or even backpacks. The transmit antennas transmit probing waveforms, which impinge on targets and are reflected back. A fusion center collects the target echo measurements of all receive antennas and jointly processes the signals to extract the desired target parameters. This dissertation proposes to address the following two bottleneck issues associated with networked radars. Reliable surveillance requires collection, communication and process of vast amounts of data. This is a power and bandwidth consuming task, which can be especially taxing in scenarios in which the antennas are on battery operated devices and are connected to the fusion center via a wireless link. Sparse sensing techniques are used to substantially reduce the amount of data that need to be communicated to a fusion center, while ensuring high target detection and estimation performance. In the first part, this dissertation derives the theoretical requirements and performance guarantees for the application of compressive sensing to both MIMO radar settings, namely, the collocated MIMO radars and the distributed MIMO radars. Confirming previous simulations based observations, the theoretical results of this thesis show that exploiting the sparsity of the target vector can reduce the amount of measurements needed for successful target estimation. For compressive sensing based distributed MIMO radars, we also propose two low-complexity signal recovery approaches. With the increasing demand of radio spectrum, the operating frequency bands of communication and radar systems often overlap, causing one system to exert interference to the other. Uncoordinated interference from communication systems may significantly harm the tactical radar functionality and vice versa. In the second part, this dissertation studies spectrum sharing between a MIMO communication system and a MIMO radar system in various scenarios. First, a cooperative spectrum sharing framework is proposed for the coexistence of MIMO radars and wireless communications. Radar transmit precoding and adaptive communication transmission are adopted, and are jointly designed to maximize signal-to-interference-plus-noise ratio (SINR) at the radar receiver subject to the communication system meeting certain rate and power constraints. Compared to the non-cooperative approaches in the literature, the proposed approach has the potential to improve the spectrum utilization because it introduces more degrees of freedom. In addition, the proposed spectrum sharing framework considers several practical issues which are not addressed in literature, e.g., the radar pulsed transmit pattern, targets falling in different range bins, and radar systems operating in the presence of clutter. Second, we investigate spectrum sharing between a MIMO communication system and a recently proposed sparse sensing based radar, namely the matrix completion based MIMO radar (MIMO-MC). MIMO-MC radar receivers take sub-Nyquist rate samples, and transfer them to a fusion center where the full data matrix is completed with high accuracy. MIMO-MC radars, in addition to reducing communication bandwidth and power as compared to MIMO radars, offer a significant advantage for spectrum sharing. The advantage stems from the way the sub-sampling scheme at the radar receivers modulates the interference channel from the communication system transmitters, rendering it symbol dependent and reducing its row space. This makes it easier for the communication system to design its waveforms in an adaptive fashion so that it minimizes the interference to the radar subject to meeting rate and power constraints. Two methods are investigated to minimize the effective interference power to the radar receiver: 1) design the communication transmit covariance matrix with fixed the radar sampling scheme, and 2) jointly design the communication transmit covariance matrix and the MIMO-MC radar sampling scheme. Furthermore, we investigate joint transmit precoding for the co-existence of a MIMO-MC radar and a MIMO wireless communication system in the presence of clutter. We show that the error performance of matrix completion in MIMO-MC radars is theoretically guaranteed when precoding is employed. The radar transmit precoder, the radar sub-sampling scheme, and the communication transmit covariance matrix are jointly designed to maximize the radar SINR while meeting certain communication rate and power constraints. Efficient optimization algorithms are provided along with insight on the proposed design problem.
This Special Issue serves as a compendium of the most recent advancements in the field of multiple-input multiple-output (MIMO) radars, including a wide array of high-potential research domains. These areas span across both theoretical frameworks and practical applications. Its key focal points include MIMO radar waveform design, MIMO signal detection, parameter estimation and imaging, as well as MIMO clutter and jamming suppression techniques. The primary objective of this Special Issue is to stimulate the generation of innovative ideas while fostering enhanced communication and collaboration opportunities among students and researchers actively involved in the field of MIMO radar research.
The research presented in this thesis deals with the concepts of distributed sensing for multiple-input multiple-output (MIMO) radar systems and important signal processing algorithms with regard to multiple sensing optimisations. These novel algorithms include an edge detection scheme based on the phase stretch transform (PST) for synthetic aperture radar (SAR) imaging systems, the application of the fractional Fourier transform (FrFT) in generating new waveform libraries and the synthesis of a generalised MIMO ambiguity function (AF) based on the Kullback-Leibler divergence (KLD). In particular, a new edge detection algorithm for SAR images is proposed. This method is an enhanced scheme that is based on the phase stretch transform (PST). The high-accuracy of the presented edge detection method is tested and verified experimentally using two SAR image datasets. Experimental results show that thresholding and further morphological operation leads in excellent edge extraction despite the noise embedded into the image. Including PST into the structure of the edge detection algorithm is proved to be very advantageous, since the efficiency in edge determining could be improved by means of tuning the strength and wrap parameters of PST phase kernel. It is shown that the proposed method is very effective and capable to remove embedded noise and introduced artefacts even from image parts corresponding to the surface of the sea. A novel waveform design scheme is proposed to create waveform libraries employing the FrFT. Additionally an efficient algorithm based on a modified Gerchberg-Saxton algorithm (MGSA) is developed to reconstruct the proposed fractional waveform libraries under constant envelope (CE) constrain. This efficient technique is capable of generating novel libraries of phase-coded waveforms through FrFT and optimise the signal retrieval, while the signal waveforms retain their constant modulus. Specifically, the reconstruction of sequences from the FrFTbased waveforms is achieved by means of the error reduction algorithm (ERA). The performance of this new method is evaluated via simulation analysis, showing the good properties of the waveforms in terms of AF performance parameters and in attaining high diversity between waveforms for both fractional and CE fractional libraries. In addition, the applicability of the derived fractional waveforms is experimentally validated, while their performance is evaluated through comparing with conventional techniques in a distributed MIMO radar scenario. Moreover, a novel-multiplexing scheme also based on the FrFT is introduced enabling radar systems to operate in a message exchange mode via embedding the required information into fractional waveforms. The efficiency of the proposed waveform design is evaluated regarding the AF properties of the communicating radar (Co-Radar) waveform. A new, generalised AF is presented based on the KLD and applied in a MIMO radar signal model. The proposed MIMO AF can be factorised into auto-correlation and cross-correlation signal matrices, and channel correlation matrices. Moreover, it is shown that the proposed MIMO AF maximally stretches between 0 and 1, while also being flexible for various geometrical and operating signal configurations. The relationship of the proposed MIMO AF with other definition is also examined, showing that it reduces to the traditional Woodward definition when the same signal model is assumed. In addition, the behaviour of the proposed MIMO AF is investigated for different target placements and operating waveforms highlighting the advantages of each configuration. Finally, the good performance of the AF is demonstrated in a simulated MIMO radar system.
This is the first book to discuss current and future applications of waveform diversity and design in subjects such as radar and sonar, communications systems, passive sensing, and many other technologies. Waveform diversity allows researchers and system designers to optimize electromagnetic and acoustic systems for sensing, communications, electronic warfare or combinations thereof. This book enables solutions to problems, explaining how each system performs its own particular function, as well as how it is affected by other systems and how those other systems may likewise be affected. It is an excellent standalone introduction to waveform diversity and design, which takes a high potential technology area and makes it visible to other researchers, as well as young engineers.
Signal Processing for Joint Radar Communications A one-stop, comprehensive source for the latest research in joint radar communications In Signal Processing for Joint Radar Communications, four eminent electrical engineers deliver a practical and informative contribution to the diffusion of newly developed joint radar communications (JRC) tools into the sensing and communications communities. This book illustrates recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithms and applications of JRC from diverse perspectives, including waveform design, physical layer processing, privacy, security, hardware prototyping, resource allocation, and sampling theory. The distinguished editors bring together contributions from more than 40 leading JRC researchers working on remote sensing, electromagnetics, optimization, signal processing, and beyond 5G wireless networks. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowing readers to take full advantage of JRC systems. Readers will also find: Thorough introductions to fundamental limits and background on JRC theory and applications, including dual-function radar communications, cooperative JRC, distributed JRC, and passive JRC Comprehensive explorations of JRC processing via waveform analyses, interference mitigation, and modeling with jamming and clutter Practical discussions of information-theoretic, optimization, and networking aspects of JRC In-depth examinations of JRC applications in cutting-edge scenarios including automotive systems, intelligent reflecting surfaces, and secure parameter estimation Perfect for researchers and professionals in the fields of radar, signal processing, communications, information theory, networking, and electronic warfare, Signal Processing for Joint Radar Communications will also earn a place in the libraries of engineers working in the defense, aerospace, wireless communications, and automotive industries.