Cognitive radar networks (CRNs) were first proposed in 2006 by Simon Haykin, shortly after the introduction of cognitive radar. In order for CRNs to benefit from many of the optimization techniques developed for cognitive radar, they must have some method of coordination and control. Both centralized and distributed architectures have been proposed, and both have drawbacks. This work addresses gaps in the literature by providing the first consideration of the problems that appear when typical cognitive radar tools are extended into networks. This work first examines the online learning techniques available to distributed CRNs, enabling optimal resource allocation without requiring a dedicated communication resource. While this problem has been addressed for single-node cognitive radar, we provide the first consideration of mutual interference in such networks. We go on to propose the first hybrid cognitive radar network structure which takes advantage of central feedback while maintaining the benefits of distributed networks. Then, we go on to investigate a novel problem of timely updating in CRNs, addressing questions of target update frequency and node updating methods. We draw from the Age of Information literature to propose Bellman-optimal solutions. Finally, we introduce the notion of mode control, and develop a way to select between active and passive target observation. / Doctor of Philosophy / Cognitive radar was inspired by biological models, where animals such as dolphins or bats use vocal pulses to form a model of their environment. As these animals seek after prey, they use information they observe to modify their vocal pulses. Cognitive radar networks are an extension of this model to a group of radar devices, which must work together cooperatively to detect and track targets. As the scene changes in time, the radar nodes in the cognitive radar network must change their operating parameters to continue performing well. This networked problem has issues not present in the single-node cognitive radar problem. In particular, as each node in the network changes operating parameters, it risks degrading the performance of the other nodes. In the contribution of this dissertation, we investigate the techniques that a cognitive radar network can use to avoid these cases of mutual performance degradation, and in particular, we investigate how this can be done without advance coordination between the nodes. In the second contribution, we go on to explore what performance improvements are available as central control is introduced. The third and fourth contributions investigate further efficiencies available to a cognitive radar network. The third contribution discusses how a resource-constrained network should communicate updates to a central aggregator. Lastly, the fourth contribution investigates additional estimation tools available to such a network, and how the network should choose between these modes.

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In recent years previously restricted radio-frequency spectrum has been opened to civilian and industrial access in the United States. Because of this, high priority users such as the military and government need to develop systems that can adapt to the surrounding spectral environment which will suddenly be filled with new users. This thesis considers an environment with one tracking radar, a single target, and a communications system that can passively interfere with the radar system. Three separate agents, Sense and Avoid, Machine Learning, and "Optimal", are tasked with the channel selection problem for radar communications coexistence. Each agent is evaluated based on their ability to detect and avoid the interferer while also tracking a target accurately. In particular, in this thesis, we are interested in the value that machine learning algorithms can provide over and above simple approaches. This value is assessed based on the conflicting requirements of avoiding interference yet using as much of the spectrum for tracking as possible. / Master of Science / With a newfound dependence on wireless transmission, the demand for electromagnetic spectrum allocations has vastly increased. In recent years the Federal Communications Commission has auctioned some previously restricted access frequency bands to public and commercial applications. While this enables the growth of faster and more widespread civilian communications, military radar systems which had been the priority users of those bands are now at risk of interference from new users. Current radar systems typically occupy fixed bands and are not yet well adjusted to sharing their allocated spectrum with other users. Cognitive radar systems have been proposed to monitor airwaves for potential interferences and autonomously manage band allocation to avoid the interferers. In this thesis, we study a learning algorithm that enables a radar system to actively monitor and select its bandwidth to ensure proper target tracking. In particular, we are interested in the value this learning algorithm can provide over and above simple approaches. This value is assessed based on the conflicting requirements of avoiding interference yet using as much of the spectrum for tracking as possible.

Cognitive Radar (CR) is a paradigm shift from a traditional radar system in that previous knowledge and current measurements obtained from the radar channel are used to form a probabilistic understanding of its environment. Moreover, CR incorporates this probabilistic knowledge into its task priorities to form illumination and probing strategies thereby rendering it a closed-loop system. Depending on the hardware's capabilities and limitations, there are various degrees of freedom that a CR may utilize. Here we will concentrate on two: temporal, where it is manifested in adaptive waveform design; and spatial, where adaptive beamsteering is used for search-and-track functions. This work is divided into three parts. First, comprehensive theory of SNR and mutual information (MI) matched waveform design in signal-dependent interference is presented. Second, these waveforms are used in a closed-loop radar platform performing target discrimination and target class identification, where the extended targets are either deterministic or stochastic. The CR's probabilistic understanding is updated via the Bayesian framework. Lastly, we propose a multiplatform CR network for integrated search-and-track application. The two radar platforms cooperate in developing a four-dimensional probabilistic understanding of the channel. The two radars also cooperate in forming dynamic spatial illumination strategy, where beamsteering is matched to the channel uncertainty to perform the search function. Once a target is detected and a track is initiated, track information is integrated into the beamsteering strategy as part of CR's task prioritization.

<P> For over six decades, the theory and design of radar systems have been dominated by probability theory and statistics, information theory, signal processing and control. However, the similar encoding-decoding property that exists between the visual brain and radar has been sadly overlooked in all radar systems. This thesis lays down the foundation of a new generation of radar systems, namely cognitive radar, that was described in a 2006 seminal paper by Haykin. Four essential elements of cognitive radar are Bayesian filtering in the receiver, dynamic programming in the transmitter, memory, and global feedback to facilitate computational intelligence. All these elements excluding the memory compose a well known property of mammalian cortex, the perception-action cycle. As such, the cognitive radar that has only this cycle is named as the basic cognitive radar (BCR). For t racking applications, t his thesis presents the underlying theory of BCR, with emphasis being placed on the cubature Kalman filter to approximate the Bayesian filter in the receiver, dynamic optimization for transmit-waveform selection in the transmitter, and global feedback embodying the transmitter , the radar environment, and the receiver all under one overall feedback loop. </p> <p> Built on the knowledge learnt from the BCR, this thesis expands the basic perception-action cycle to encompass three more properties of human cognition , that is, memory, attention, and intelligence. Specifically, the provision for memory includes the three essential elements, i. e. , the perceptual memory, executive memory, and coordinating perception-action memory that couples the first two memories. Provision of the three memories adds an advanced version of cognitive radar, namely the nested cognitive radar (NCR) in light of the nesting of three memories in the perception-action cycle. </p> <p> In this thesis, extensive computer simulations are also conducted to demonstrate the ability of this new radar concept over a conventional radar structure. Three important scenarios of tracking applications are considered, they are (a), linear target tracking; (b), falling object tracking; and (c), high-dimensional target tracking with continuous-discrete model. All simulation results confirm that cognitive radar outperforms the conventional radar systems significantly. </p> <p> In conducting the simulations, an interesting phenomenon is also observed, which is named the chattering effect. The underlying physics and mathematical model of this effect are discussed. For the purpose of studying the behaviour of cognitive radar in disturbance, demonstrative experiments are further conducted. Simulation results indicate the superiority of NCR over BCR and t he conventional radar in low, moderate and even strong disturbance. </p> / Thesis / Doctor of Philosophy (PhD)

This dissertation shows the performance of adaptive waveforms when applied to two radar applications. One application is automatic target recognition (ATR) and the other application is range-Doppler ambiguity mitigation. The adaptive waveforms are implemented via a feedback loop from receiver to transmitter, such that previous radar measurements affect how the adaptive waveforms proceed. For the ATR application, adaptive transmitter can change the waveform's temporal structure to improve target recognition performance. For range-Doppler ambiguity mitigation application, adaptive transmitter can change the pulse repetition frequency (PRF) to mitigate range and Doppler ambiguity. In the ATR application, commercial electromagnetic software is used to create high-fidelity aircraft target signatures. Realistic waveform constraints are applied to show radar performance. The radar equation is incorporated into the waveform design technique and template-based classification is performed. Translation invariant feature is used for inaccurately known range scenario. The performance of adaptive waveforms is evaluated with not only a monostatic radar, but also widely separated MIMO radar. In MIMO radar, multiple transmit waveforms are used, but spectral leakage caused by constant-modulus constraint shows minimal interference effect. In the range-Doppler ambiguity mitigation application, particle-filter-based track-before-detect for a single target is extended to track and detect multiple low signal-to-noise ratio (SNR) targets, simultaneously. To mitigate ambiguity, multiple PRFs are used and improved PRF selection technique is implemented via predicted entropy computation when both blind and clutter zones are considered.

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In this paper we address radar-communication coexistence by modelling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system to improve its performance. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations. In this thesis we also address the application of the MDP into a software defined radio (SDR) USRP X310 by utilizing the software LabVIEW to communicate with and control the SDR. / Master of Science / In this thesis we develop methods for creating and implementing algorithms for a cognitive radar. A cognitive radar is a radar that is able to sense its environment and avoid any other communication system that may interfere with its operation. We discuss the predictive methods we used to sense and avoid the other communication systems as well as how we implemented this using a software defined radar based on the USRP X310.