Multiple-input multiple-output (MIMO) radar employs orthogonal or partially correlated transmit signals to achieve performance benefits over its phased-array counterpart. It has been shown that MIMO radar can achieve greater spatial resolution, improved signal-to-noise ratio (SNR) and target localization, and greater clutter resolution using space-time adaptive processing (STAP). This thesis explores various methods to improve the signal-to-interference-plus-noise ratio (SINR) via transmit and receive beamforming. In MIMO radar settings, it is often desirable to transmit power only to a given location or set of locations defined by a beampattern. Current methods involve a two- step process of designing the transmit covariance matrix R via iterative solutions and then using R to generate waveforms that fulfill practical constraints such as having a constant-envelope or drawing from a finite alphabet. In this document, a closed- form method to design R is proposed that utilizes the discrete Fourier transform (DFT) coefficients and Toeplitz matrices. The resulting covariance matrix fulfills the practical constraints such as positive semidefiniteness and the uniform elemental power constraint and provides performance similar to that of iterative methods, which require a much greater computation time. Next, a transmit architecture is presented 
that exploits the orthogonality of frequencies at discrete DFT values to transmit a sum of orthogonal signals from each antenna. The resulting waveforms provide a lower mean-square error than current methods at a much lower computational cost, and a simulated detection scenario demonstrates the performance advantages achieved. It is also desirable to receive signal power only from a given set of directions defined by a beampattern. In a later chapter of this document, the problem of receive beampattern matching is formulated and three solutions to this problem are demonstrated. We show that partitioning the received data vector into subvectors and then multiplying each subvector with its corresponding weight vector can improve performance and reduce the length of the data vector. Simulation results show that all methods are capable of matching a desired beampattern. Signal-to-interference- plus-noise ratio (SINR) calculations demonstrate a significant improvement over the unaltered MIMO case.

Multiple Input Multiple Output (MIMO) radar systems has attracted lately a lot of attention thanks to its advantage over the classical phased array radar systems. We site among these advantages the improvement of parametric identifiability, achievement of higher spatial resolution and design of complex beampatterns. In colocated multiple-input multiple-output radar systems, it is usually desirable to steer transmitted power in the region-of-interest in order to increase the Signal to Noise Ratio (SNR) and reduce any undesired signal and thus improve the detection process. This problem is also known as transmit beampattern design. To achieve this goal, conventional methods optimize the waveform covariance matrix, R, for the desired beampattern, which is then used to generate the actual transmitted waveforms. Both steps require constrained optimization. Most of the existing methods use iterative algorithms to solve these problems, therefore their computational complexity is very high which makes them hard to use in practice especially for real time radar applications. In this paper, we provide a closed-form solution to design the covariance matrix for a given beampattern in the three dimensional space using planar arrays, which is then used to derive a novel closed-form algorithm to directly design the finite-alphabet constant-envelope waveforms. The proposed algorithm exploits the two-dimensional discrete Fourier transform which is implemented using fast Fourier transform algorithm. Consequently, the computational complexity of the proposed beampattern solution is very low allowing it to be used for large arrays to change the beampattern in real time. We also show that the number of required snapshots in each waveform depends on the beampattern and that it is less than the total number of transmit antennas. In addition, we show that the proposed waveform design method can be used with non symmetric beampatterns. The performance of our proposed algorithm compares favorably with the existing iterative methods in terms of mean square error.

Multiple-input multiple-output (MIMO) radar has been receiving increasing attention in recent years due to the dramatic advantages offered by MIMO systems in communications. The amount of energy reflected from a common radar target varies considerably with the observation angle, and these scintillations may cause signal fading which severely degrades the performance of conventional radars. MIMO radar with widely spaced antennas is able to view several aspects of a target simultaneously, which realizes a spatial diversity gain to overcome the target scintillation problem, leading to significantly enhanced system performance. Building on the initial studies presented in the literature, MIMO radar is investigated in detail in this thesis. First of all, a finite scatterers model is proposed, based on which the target detection performance of a MIMO radar system with arbitrary array-target configurations is evaluated and analyzed. A MIMO radar involving a realistic target is also set up, whose simulation results corroborate the conclusions drawn based on theoretical target models, validating in a practical setting the improvements in detection performance brought in by the MIMO radar configuration. Next, a hybrid bistatic radar is introduced, which combines the phased-array and MIMO radar configurations to take advantage of both coherent processing gain and spatial diversity gain simultaneously. The target detection performance is first assessed, followed by the evaluation of the direction finding performance, i.e., performance of estimating angle of arrival as well as angel of departure. The presented theoretical expressions can be used to select the best architecture for a radar system, particularly when the total number of antennas is fixed. Finally, a novel two phase radar scheme involving signal retransmission is studied. It is based on the time-reversal (TR) detection and is investigated to improve the detection performance of a wideband MIMO radar or sonar system. Three detectors demanding various amounts of a priori information are developed, whose performance is evaluated and compared. Three schemes are proposed to design the retransmitted waveform with constraints on the transmitted signal power, further enhancing the detection performance with respect to the TR approach.

abstract: In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted mean-squared error (MSE). As a result, the radar parameters are adaptively and optimally selected for superior performance. Based on previous work, this thesis highlights the applicability of the predicted covariance matrix to the lower SNR waveform-agile tracking problem. The adaptive waveform selection algorithm's MSE performance was compared against fixed waveforms using Monte Carlo simulations. It was found that the adaptive approach performed at least as well as the best fixed waveform when focusing on estimating only position or only velocity. When these estimates were weighted by different amounts, then the adaptive performance exceeded all fixed waveforms. This improvement in performance demonstrates the utility of the predicted covariance in waveform design, at low SNR conditions that are poorly handled with more traditional tracking algorithms. / Dissertation/Thesis / M.S. Electrical Engineering 2011

Cette thèse se focalise sur le concept du radar MIMO co-localisé. L'acronyme MIMO -- pour Multiple-Input Multiple-Output -- indique l'utilisation de plusieurs émetteurs et de plusieurs récepteurs, tandis que le terme co-localisé signifie que ces éléments sont étroitement espacés. Chaque émetteur envoie une forme d'onde qui lui est propre : un radar MIMO émet donc simultanément un ensemble de signaux.Cette thèse a ainsi pour but d'établir une méthodologie permettant de générer cet ensemble de signaux, tout en respectant certaines contraintes opérationnelles. Cela nous permettra de déterminer les apports éventuels de ce radar. Nous nous sommes intéressés en particulier aux codes de phase, pour des raisons de couplage (qu'on peut traduire ici par la capacité, lors du traitement, à distinguer la position angulaire d'une cible de sa distance).La méthodologie proposée se synthétise simplement en une modélisation sous la forme d'un problème d'optimisation. Contrairement à la littérature et à des précédents résultats théoriques, nous avons décidé d'évaluer l'orthogonalité des signaux émis par le radar en différentes directions, et non l'orthogonalité des signaux élémentaires. Ce problème, plus réaliste, est malheureusement non-convexe et à grande échelle : un benchmark sur différentes méthodes d'optimisation nous a permis de constater l'efficacité des algorithmes basées sur le gradient.Optimiser cette orthogonalité sous-entend l'utilisation de filtres adaptés. Cependant, en pratique, le traitement radar s'effectue à l'aide de filtres désadaptés. Nous suggérons ainsi un problème d'optimisation jointe, permettant de générer de manière simultanée un ensemble de formes d'onde (pour le radar MIMO, entre autres) et les filtres désadaptés associés. Des simulations ont permis de montrer l'efficacité de la méthode. Celle-ci est en particulier préférable aux algorithmes cycliques habituellement utilisés. / This thesis deals with coherent MIMO radars. MIMO stands for Multiple-Input Multiple-Output, meaning that several transmitters and several receivers are used, closely-spaced in a coherent MIMO radar. Each transmitter has its own signal, providing waveform diversity. This thesis aims for defining a way to generate a set of sequences, specific for this radar, while satisfying practical constraints. It may help to determine the potential contribution of a MIMO radar. Only phase codes are concerned here, because they suffer less from the range/angle coupling effect.A simple framework is introduced, based on an optimisation problem.While literature often involves the orthogonality of the elementary signals (because of theoretical aspects), it is suggested to consider the orthogonality of signals from different directions of the surveillance space. Unfortunately, the obtained optimisation problem is non-convex and has a lot of variables. A benchmark on a simpler problem notifies us that gradient-based algorithms are surprisingly efficient.An optimisation of the correlation function corresponds to a processing with matched filters. However, in practice, mismatched filters are usually employed. A joint optimisation problem is suggested accordingly, in order to generate simultaneously a set of sequences (e.g. MIMO radar signals) and their associated mismatched filters. Obtained results are quite promising : as expected, a joint optimisation seems to perform better than a cyclic one, usually employed.

Three information measures are discussed and used as objective functions for optimization of wireless sensor networks (WSNs) and radar systems. In addition, a long-term system performance measure is developed for evaluating the performance of slow-fading WSNs. Three system applications are considered: a distributed detection system, a distributed multiple hypothesis system, and a radar target recognition system.First, we consider sensor power optimization for distributed binary detection systems. The system communicates over slow-fading orthogonal multiple access channels. In earlier work, it was demonstrated that system performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system. We define outage probability for slow-fading system as a long-term performance measure, and analytically develop the detection outage with the given system model.Based on the analytical result of the outage probability, diversity gain is derived and shown to be proportional to the number of the sensor nodes. Then, we extend the optimized power control strategy to a distributed multiple hypothesis system, and enhance the power optimization by exploiting a priori probabilities and local sensor statistics. We also extend outage probability to the distributed multiple-hypotheses problem. The third application is radar waveform design with a new performance measure: Task-Specific Information (TSI). TSI is an information-theoretic measure formulated for one or more specific sensor tasks by encoding the task(s) directly into the signal model via source variables. For example, we consider the problem of correctly classifying a linear system from a set of known alternatives, and the source variable takes the form of an indicator vector that selects the transfer function of the true hypothesis. We then compare the performance of TSI with conventional waveforms and other information-theoretic waveform designs via simulation. We apply radar-specific constraints and signal models to the waveform optimization.

abstract: In recent years, there has been an increased interest in sharing available bandwidth to avoid spectrum congestion. With an ever-increasing number wireless users, it is critical to develop signal processing based spectrum sharing algorithms to achieve cooperative use of the allocated spectrum among multiple systems in order to reduce interference between systems. This work studies the radar and communications systems coexistence problem using two main approaches. The first approach develops methodologies to increase radar target tracking performance under low signal-to-interference-plus-noise ratio (SINR) conditions due to the coexistence of strong communications interference. The second approach jointly optimizes the performance of both systems by co-designing a common transmit waveform. When concentrating on improving radar tracking performance, a pulsed radar that is tracking a single target coexisting with high powered communications interference is considered. Although the Cramer-Rao lower bound (CRLB) on the covariance of an unbiased estimator of deterministic parameters provides a bound on the estimation mean squared error (MSE), there exists an SINR threshold at which estimator covariance rapidly deviates from the CRLB. After demonstrating that different radar waveforms experience different estimation SINR thresholds using the Barankin bound (BB), a new radar waveform design method is proposed based on predicting the waveform-dependent BB SINR threshold under low SINR operating conditions. A novel method of predicting the SINR threshold value for maximum likelihood estimation (MLE) is proposed. A relationship is shown to exist between the formulation of the BB kernel and the probability of selecting sidelobes for the MLE. This relationship is demonstrated as an accurate means of threshold prediction for the radar target parameter estimation of frequency, time-delay and angle-of-arrival. For the co-design radar and communications system problem, the use of a common transmit waveform for a pulse-Doppler radar and a multiuser communications system is proposed. The signaling scheme for each system is selected from a class of waveforms with nonlinear phase function by optimizing the waveform parameters to minimize interference between the two systems and interference among communications users. Using multi-objective optimization, a trade-off in system performance is demonstrated when selecting waveforms that minimize both system interference and tracking MSE. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

The topic of multiple input multiple output (MIMO) radar recently gained considerable interest because it can transmit partially correlated or fully independent waveforms. The inherited waveform diversity helps MIMO radars identify more targets and adds flexibility to the beampattern design. The realized advantages come at the expense of enhanced processing requirements and increased system complexity. In this regards, a closed-form method is derived to generate practical finite-alphabet waveforms with specific correlation properties to match the desired beampattern. Next, the performance of adaptive estimation techniques is examined. Indeed, target localization or reflection coefficient estimation usually involves optimizing a given cost-function over a grid of points. The estimation performance is directly affected by the grid resolution. In this work, the cost function of Capon and amplitude and phase estimation (APES) adaptive beamformers are reformulated. The new cost functions can be evaluated using the two-dimensional fast-Fourier-transform (2D-FFT) which reduces the estimation runtime. Generalized expressions of the Cram´er-Rao lower bound are computed to assess the performance of our estimators. Afterward, a novel estimation algorithm based on the monopulse technique is proposed. In comparison with adaptive methods, monopulse requires less number of received pulses. Hence, it is widely used for fast target localization and tracking purposes. This work suggests an approach that localizes two point targets present in the hemisphere using one set of four antennas. To separate targets sharing the same elevation or azimuth angles, a second set of antennas is required. Two solutions are suggested to combine the outputs from the antenna sets and improve the overall detection performance. The last part of the dissertation focuses on the application and implementation side of radars rather than the theoretical aspects. It describes the realized hardware and software design of a compact portable 24 GHz frequency-modulated-continuous-wave (FMCW) radar. The prototype can assist the visually impaired during their outdoor journeys and prevents collisions with their surrounding environment. Moreover, the device performs diverse tasks such as range-direction mapping, velocity estimation, presence detection, and vital sign monitoring. The experimental result section demonstrates the device’s capabilities in different use-cases.

This thesis work consists of two research projects. The first project presented is on waveform design for car radars. These radars are used to detect other vehicles to avoid collision. In this project, we attempt to find the best waveform that distinguishes large objects from small ones. This helps the radar system reach more reliable decisions. We consider several models of the problem with varying complexity. For each model, we present optimization results calculated under various constraints regarding how the waveform is generated and how the reflected signal is processed. The results show that changing the radar waveform can result in better target classification. The second project is about digital-to-analog converter (DAC) linearization. Ideally, DACs have a linear input-output relation. In practice, however, this relation is nonlinear which may be harmful for many applications. A more linear input-output relation can be achieved by modifying the input to a DAC. This method, called predistortion, requires a good understanding of how DAC errors contribute to the nonlinearity. Assuming a simple DAC model, we investigate how different error functions lead to different types of nonlinearities through theoretical analyses and supporting computer simulations. We present our results in terms of frequency spectrum calculations. We show that the nonlinearity observed at the output strongly depends on how the error is modeled. These results are helpful in designing a predistorter for linearization.

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