Information Diversity in Coherent MIMO Radars

In this paper, the concept of information diversity in both the space and frequency domains is investigated for multiple-input multiple-output (MIMO) radars with widely separated antennas. Compared to phased-antenna arrays and multistatic radars, they can exploit more degrees of freedom, allowing them to maximize the information content upon centralized data fusion, thus granting unprecedented target detection and localization capabilities.This analysis proceeds in parallel with the running progresses of microwave photonics (MWP), which could represent, in the near future, a new paradigm for the development of centralized MIMO radar architectures.Thus, understanding the implications of information diversity becomes essential to foretell the system effectiveness in detecting and resolving closely spaced targets, as well as in suppressing sidelobes which may lead to false alarms. Performance metrics are proposed and evaluated to characterize the effects that information diversity has on centralized MIMO radars with widely separated antennas. On the other hand, the proposed methodology could reveal precious for designing the optimum system configuration

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

System design of the MeerKAT L - band 3D radar for monitoring near earth objects

This thesis investigates the current knowledge of small space debris (diameter less than 10 cm) and potentially hazardous asteroids (PHA) by the use of radar systems. It clearly identifies the challenges involved in detecting and tracking of small space debris and PHAs. The most significant challenges include: difficulty in tracking small space debris due to orbital instability and reduced radar cross-section (RCS), errors in some existing data sets, the lack of dedicated or contributing instruments in the Southern Hemisphere, and the large cost involved in building a high-performance radar for this purpose. This thesis investigates the cooperative use of the KAT-7 (7 antennas) and MeerKAT (64 antennas) radio telescope receivers in a radar system to improve monitoring of small debris and PHAs was investigated using theory and simulations, as a cost-effective solution. Parameters for a low cost and high-performance radar were chosen, based on the receiver digital back-end. Data from such radars will be used to add to existing catalogues thereby creating a constantly updated database of near Earth objects and bridging the data gap that is currently being filled by mathematical models. Based on literature and system requirements, quasi-monostatic, bistatic, multistatic, single input multiple output (SIMO) radar configurations were proposed for radio telescope arrays in detecting, tracking and imaging small space debris in the low Earth orbit (LEO) and PHAs. The maximum dwell time possible for the radar geometry was found to be 30 seconds, with coherent integration limitations of 2 ms and 121 ms for accelerating and non-accelerating targets, respectively. The multistatic and SIMO radar configurations showed sufficient detection (SNR 13 dB) for small debris and quasi-monostatic configuration for PHAs. Radar detection, tracking and imaging (ISAR) simulations were compared to theory and ambiguities in range and Doppler were compensated for. The main contribution made by this work is a system design for a high performance, cost effective 3D radar that uses the KAT-7 and MeerKAT radio telescope receivers in a commensal manner. Comparing theory and simulations, the SNR improvement, dwell time increase, tracking and imaging capabilities, for small debris and PHAs compared to existing assets, was illustrated. Since the MeerKAT radio telescope is a precursor for the SKA Africa, extrapolating the capabilities of the MeerKAT radar to the SKA radar implies that it would be the most sensitive and high performing contributor to space situational awareness, upon its completion. From this feasibility study, the MeerKAT 3D distributed radar will be able to detect debris of diameter less than 10 cm at altitudes between 700 km to 900 km, and PHAs, with a range resolution of 15 m, a minimum SNR of 14 dB for 152 pulses for a coherent integration time of 2.02 ms. The target range (derived from the two way delay), velocity (from Doppler frequency) and direction will be measured within an accuracy of: 2.116 m, 15.519 m/s, 0.083° (single antenna), respectively. The range, velocity accuracies and SNR affect orbit prediction accuracy by 0.021 minutes for orbit period and 0.0057° for orbit inclination. The multistatic radar was found to be the most suitable and computationally efficient configuration compared to the bistatic and SIMO configurations, and beamforming should be implemented as required by specific target geometry

Target localization in MIMO radar systems

MIMO (Multiple-Input Multiple-Output) radar systems employ multiple antennas to transmit multiple waveforms and engage in joint processing of the received echoes from the target. MIMO radar has been receiving increasing attention in recent years from researchers, practitioners, and funding agencies. Elements of MIMO radar have the ability to transmit diverse waveforms ranging from independent to fully correlated. MIMO radar offers a new paradigm for signal processing research. In this dissertation, target localization accuracy performance, attainable by the use of MIMO radar systems, configured with multiple transmit and receive sensors, widely distributed over an area, are studied. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. It is shown that optimization over the sensors\u27 positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE\u27s utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area. Coherent processing advantage for target localization relies on time and phase synchronization between transmitting and receiving radars. An analysis of the sensitivity of the localization performance with respect to the variance of phase synchronization error is provided by deriving the hybrid CRLB. The single target case is extended to the evaluation of multiple target localization performance. Thus far, the analysis assumes a stationary target. Study of moving target tracking capabilities is offered through the use of the Bayesian CRLB for the estimation of both target location and velocity. Centralized and decentralized tracking algorithms, inherit to distributed MIMO radar architecture, are proposed and evaluated. It is shown that communication requirements and processing load may be reduced at a relatively low performance cost

Mathematical optimization and game theoretic methods for radar networks

Radar systems are undoubtedly included in the hall of the most momentous discoveries of the previous century. Although radars were initially used for ship and aircraft detection, nowadays these systems are used in highly diverse fields, expanding from civil aviation, marine navigation and air-defence to ocean surveillance, meteorology and medicine. Recent advances in signal processing and the constant development of computational capabilities led to radar systems with impressive surveillance and tracking characteristics but on the other hand the continuous growth of distributed networks made them susceptible to multisource interference. This thesis aims at addressing vulnerabilities of modern radar networks and further improving their characteristics through the design of signal processing algorithms and by utilizing convex optimization and game theoretic methods. In particular, the problems of beamforming, power allocation, jammer avoidance and uncertainty within the context of multiple-input multiple-output (MIMO) radar networks are addressed. In order to improve the beamforming performance of phased-array and MIMO radars employing two-dimensional arrays of antennas, a hybrid two-dimensional Phased-MIMO radar with fully overlapped subarrays is proposed. The work considers both adaptive (convex optimization, CAPON beamformer) and non-adaptive (conventional) beamforming techniques. The transmit, receive and overall beampatterns of the Phased-MIMO model are compared with the respective beampatterns of the phased-array and the MIMO schemes, proving that the hybrid model provides superior capabilities in beamforming. By incorporating game theoretic techniques in the radar field, various vulnerabilities and problems can be investigated. Hence, a game theoretic power allocation scheme is proposed and a Nash equilibrium analysis for a multistatic MIMO network is performed. A network of radars is considered, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since no communication between the clusters is assumed, non-cooperative game theoretic techniques and convex optimization methods are utilized to tackle the power adaptation problem. During the proof of the existence and the uniqueness of the solution, which is also presented, important contributions on the SINR performance and the transmission power of the radars have been derived. Game theory can also been applied to mitigate jammer interference in a radar network. Hence, a competitive power allocation problem for a MIMO radar system in the presence of multiple jammers is investigated. The main objective of the radar network is to minimize the total power emitted by the radars while achieving a specific detection criterion for each of the targets-jammers, while the intelligent jammers have the ability to observe the radar transmission power and consequently decide its jamming power to maximize the interference to the radar system. In this context, convex optimization methods, noncooperative game theoretic techniques and hypothesis testing are incorporated to identify the jammers and to determine the optimal power allocation. Furthermore, a proof of the existence and the uniqueness of the solution is presented. Apart from resource allocation applications, game theory can also address distributed beamforming problems. More specifically, a distributed beamforming and power allocation technique for a radar system in the presence of multiple targets is considered. The primary goal of each radar is to minimize its transmission power while attaining an optimal beamforming strategy and satisfying a certain detection criterion for each of the targets. Initially, a strategic noncooperative game (SNG) is used, where there is no communication between the various radars of the system. Subsequently, a more coordinated game theoretic approach incorporating a pricing mechanism is adopted. Furthermore, a Stackelberg game is formulated by adding a surveillance radar to the system model, which will play the role of the leader, and thus the remaining radars will be the followers. For each one of these games, a proof of the existence and uniqueness of the solution is presented. In the aforementioned game theoretic applications, the radars are considered to know the exact radar cross section (RCS) parameters of the targets and thus the exact channel gains of all players, which may not be feasible in a real system. Therefore, in the last part of this thesis, uncertainty regarding the channel gains among the radars and the targets is introduced, which originates from the RCS fluctuations of the targets. Bayesian game theory provides a framework to address such problems of incomplete information. Hence, a Bayesian game is proposed, where each radar egotistically maximizes its SINR, under a predefined power constraint

Multistatic radar optimization for radar sensor network applications

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

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Cooperative multiterminal radar and communication: a new paradigm for 6G mobile networks

The impending spectrum congestion imposed by the emergence of new bandwidth-thirsty applications may be mitigated by the integration of radar and classic communications functionalities in a common system. Furthermore, the merger of a sensing component into wireless communication networks has raised interest in recent years and it may become a compelling design objective for 6G. This article presents the evolution of the hitherto separate radar and communication systems towards their amalgam known as a joint radar and communication (RADCOM) system. Explicitly, we propose to integrate a radio sensing component into 6G. We consider an ultra-dense network (UDN) scenario relying on an active multistatic radar configuration and on cooperation between the access points across the entire coverage area. The technological trends required to reach a feasible integration, the applications anticipated and the open research challenges are identified, with an emphasis on high-accuracy network synchronization. The successful integration of these technologies would facilitate centimeter-level resolution, hence supporting compelling high-resolution applications for next-generation networks, such as robotic cars and industrial assembly lines.publishe

Mathematical optimization techniques for cognitive radar networks

This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the exploitation of initial radar waveforms characterized by desired auto and cross-correlation properties. Finally, robust optimization techniques are introduced to account for the assumptions made by cognitive radars on certain second order statistics such as the covariance matrix of the clutter. More specifically, initial optimization techniques were proposed for the case of bistatic radars. By maximizing the signal to interference and noise ratio (SINR) under certain constraints on the transmitted signals, it was possible to iteratively optimize both the orthogonal transmission waveforms and the receiver filter. Subsequently, the above work was extended to a convex optimization framework for a waveform design technique for bistatic radars where both radars transmit and receive to detect targets. The method exploited prior knowledge of the environment to maximize the accumulated target return signal power while keeping the disturbance power to unity at both radar receivers. The thesis further proposes convex optimization based waveform designs for multiple input multiple output (MIMO) based cognitive radars. All radars within the system are able to both transmit and receive signals for detecting targets. The proposed model investigated two complementary optimization techniques. The first one aims at optimizing the signal to interference and noise ratio (SINR) of a specific radar while keeping the SINR of the remaining radars at desired levels. The second approach optimizes the SINR of all radars using a max-min optimization criterion. To account for possible mismatches between actual parameters and estimated ones, this thesis includes robust optimization techniques. Initially, the multistatic, signal-dependent model was tested against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered signal-dependent clutter scenario. Therefore a new approach was derived where uncertainty was assumed directly on the radar cross-section and Doppler parameters of the clutters. Approximations based on Taylor series were invoked to make the optimization problem convex and {subsequently} determine robust waveforms with specific SINR outage constraints. Finally, this thesis introduces robust optimization techniques for through-the-wall radars. These are also cognitive but rely on different optimization techniques than the ones previously discussed. By noticing the similarities between the minimum variance distortionless response (MVDR) problem and the matched-illumination one, this thesis introduces robust optimization techniques that consider uncertainty on environment-related parameters. Various performance analyses demonstrate the effectiveness of all the above algorithms in providing a significant increase in SINR in an environment affected by very strong clutter and noise