Some contributions on MIMO radar

Motivated by recent advances in Multiple Input Multiple Output (MIMO) wireless communications, this dissertation aims at exploring the potential of MIMO approaches in the radar context. In communications, MIMO systems combat the fading effects of the multi-path channel with spatial diversity. Further, the scattering environment can be used by such systems to achieve spatial multiplexing. In radar, a complex target consisting of several scatterers takes the place of the multi-path channel of the communication problem. A target\u27s radar cross section (RCS), which determines the amount of returned power, greatly varies with the considered aspect. Those variations significantly impair the detection and estimation performance of conventional radar employing closely spaced arrays on transmit and receive sides. In contrast, by widely separating the transmit and receive elements, MIMO radar systems observe a target simultaneously from different aspects resulting in spatial diversity. This diversity overcomes the fluctuations in received power. Similar to the multiplexing gain in communications, the simultaneous observation of a target from several perspectives enables resolving its features with an accuracy beyond the one supported by the bandwidth. The dissertation studies the MIMO concept in radar in the following manner. First, angle of arrival estimation is explored for a system applying transmit diversity on the transmit side. Due to the target\u27s RCS fluctuations, the notion of ergodic and outage Cramer Rao bounds is introduced. Both bounds are compared with simulation results revealing the diversity potentials of MIMO radar. Afterwards, the detection of targets in white Gaussian noise is discussed including geometric considerations due to the wide separation between the system elements. The detection performance of MIMO radar is then compared to the one achieved by conventional phased array radar systems. The discussion is extended to include returns from homogeneous clutter. A Doppler processing based moving target detector for MIMO radar is developed in this context. Based on this detector, the moving target detection capabilities of MIMO radar are evaluated and compared to the ones of phased array and multi-static radar systems. It is shown, that MIMO radar is capable of reliably detecting targets moving in an arbitrary direction. The advantage of using several transmitters is illustrated and the constant false alarm rate (CFAR) property of adaptive MIMO moving target detectors is demonstrated. Finally, the high resolution capabilities of MIMO radar are explored. As noted above, the several individual scatterers constituting a target result in its fluctuating RCS. The high resolution mode is aimed at resolving those scatterers. With Cramer Rao bounds and simulation results, it is explored how observing a single isotropic scatterer from several aspects enhances the accuracy of estimating the location of this scatterer. In this context a new, two-dimensional ambiguity function is introduced. This ambiguity function is used to illustrate that several scatterers can be resolved within a conventional resolution cell defined by the bandwidth. The effect of different system parameters on this ambiguity function is discussed

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

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

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

Applicability and Advantages of Implementation of MIMO Techniques in Radar Systems

High-accuracy object detection using radio frequency signal has become popular field for research since last couple of years. Huge amount of research work are being done in this field now a days. Although radar systems were invented for the purpose of military, they are also used for civil service at present. MIMO communication systems becomes popular in recent years because of higher capacity, increased coverage and better voice and data quality in telecommunication systems. The overwhelming popularity of MIMO systems draws radar researchers’ attention to study the probability of implementing MIMO techniques in radar systems. This trend has been followed in this thesis. The applicability of MIMO in radar systems has been examined along with small simulations outcomes, which ends with analysis of the result and further research probability in this field. Any type of diversity is required for MIMO radar. Some of the probable diversity techniques are discussed with a signal model along with their advantages and disadvantages. This thesis starts with a brief discussion about radar principle and different types of radar systems, followed by detailed discussion on MIMO technology and their implementation on radar systems. Angular diversity i.e. beamforming is considered, in the simulation part of the thesis, to implement MIMO. Ideal propagation environment is assumed in the simulations in order to keep the focus on the beamforming mechanism itself. Approximately 10 dB signal-to-noise ratio gain is obtained in the simulations using reasonably low number of antennas. The thesis ends up with short discussion on the advantages of MIMO application in radar along with future research possibilities in this arena

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

Adaptive MIMO Radar for Target Detection, Estimation, and Tracking

We develop and analyze signal processing algorithms to detect, estimate, and track targets using multiple-input multiple-output: MIMO) radar systems. MIMO radar systems have attracted much attention in the recent past due to the additional degrees of freedom they offer. They are commonly used in two different antenna configurations: widely-separated: distributed) and colocated. Distributed MIMO radar exploits spatial diversity by utilizing multiple uncorrelated looks at the target. Colocated MIMO radar systems offer performance improvement by exploiting waveform diversity. Each antenna has the freedom to transmit a waveform that is different from the waveforms of the other transmitters. First, we propose a radar system that combines the advantages of distributed MIMO radar and fully polarimetric radar. We develop the signal model for this system and analyze the performance of the optimal Neyman-Pearson detector by obtaining approximate expressions for the probabilities of detection and false alarm. Using these expressions, we adaptively design the transmit waveform polarizations that optimize the target detection performance. Conventional radar design approaches do not consider the goal of the target itself, which always tries to reduce its detectability. We propose to incorporate this knowledge about the goal of the target while solving the polarimetric MIMO radar design problem by formulating it as a game between the target and the radar design engineer. Unlike conventional methods, this game-theoretic design does not require target parameter estimation from large amounts of training data. Our approach is generic and can be applied to other radar design problems also. Next, we propose a distributed MIMO radar system that employs monopulse processing, and develop an algorithm for tracking a moving target using this system. We electronically generate two beams at each receiver and use them for computing the local estimates. Later, we efficiently combine the information present in these local estimates, using the instantaneous signal energies at each receiver to keep track of the target. Finally, we develop multiple-target estimation algorithms for both distributed and colocated MIMO radar by exploiting the inherent sparsity on the delay-Doppler plane. We propose a new performance metric that naturally fits into this multiple target scenario and develop an adaptive optimal energy allocation mechanism. We employ compressive sensing to perform accurate estimation from far fewer samples than the Nyquist rate. For colocated MIMO radar, we transmit frequency-hopping codes to exploit the frequency diversity. We derive an analytical expression for the block coherence measure of the dictionary matrix and design an optimal code matrix using this expression. Additionally, we also transmit ultra wideband noise waveforms that improve the system resolution and provide a low probability of intercept: LPI)

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

Implementation of a coherent real-time noise radar system

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse-Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real-time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200\ua0MHz bandwidth, and 268\ua0ms integration time while processing a range of about 8.5\ua0km. Additionally, the system can perform real-time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off-chip dynamic random access memory

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

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