Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

In this paper, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cram\'{e}r-Rao lower bound (BCRLB) and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a non-linear and non-convex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization (PSO) algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks

Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Commmunications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks

Applications of machine learning and deep learning in antenna design, optimization, and selection : a review

This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE

Adaptive Beamsteering Cognitive Radar with Integrated Search-and-Track of Swarm Targets

The article of record as published may be found at http://dx.doi.org/10.1109/ACCESS.2021.3069350, IEEE AccessAdaptive beamsteering cognitive radar (AB-CRr) systems seek to improve detection and tracking performance by formulating a beam placement strategy adapted to their environment. AB-CRr builds a probabilistic model of the target environment that enables it to more efficiently employ its limited resources to locate and track targets. In this work, we investigate methods for adapting the AB- CRr framework to detect and track large target swarms. This is achieved by integrating the properties of correlated-motion swarms into both the radar tracking model and AB-CRr’s underlying dynamic probability model. As a result, a list of newly CRr-integrated contributions are enumerated: a) improved uncertainty function design, b) incorporates Mahalanobis nearest neighbors multi-target association methodology into AB-CRr, c) introduces a novel Kalman-based consolidated swarm tracking methodology with a common velocity state vector that frames targets as a correlated collection of swarm members, d) introduces an improved uncertainty growth model for updating environment probability map, e) introduces a method for incorporating estimated swarm structure and behavior into the uncertainty update model referred to as "track hinting", and f) introduces new metrics for swarm search/detection and tracking called swarm centroid track error and swarm tracking dwell ratio. The results demonstrate that AB-CRr is capable of adapting its beamsteering strategy to efficiently perform resource balancing between target search and swarm tracking applications, while taking advantage of group structure and intra-swarm target correlation to resist large swarms overloading available resources.Approved for public release; distribution is unlimited

DISTRIBUTED OPTIMIZATION OF RESOURCE ALLOCATION FOR SEARCH AND TRACK ASSIGNMENT WITH MULTIFUNCTION RADARS

The long-term goal of this research is to contribute to the design of a conceptual architecture and framework for the distributed coordination of multifunction radar systems. The specific research objective of this dissertation is to apply results from graph theory, probabilistic optimization, and consensus control to the problem of distributed optimization of resource allocation for multifunction radars coordinating on their search and track assignments. For multiple radars communicating on a radar network, cooperation and agreement on a network resource management strategy increases the group's collective search and track capability as compared to non-cooperative radars. Existing resource management approaches for a single multifunction radar optimize the radar's configuration by modifying the radar waveform and beam-pattern. Also, multi-radar approaches implement a top-down, centralized sensor management framework that relies on fused sensor data, which may be impractical due to bandwidth constraints. This dissertation presents a distributed radar resource optimization approach for a network of multifunction radars. Linear and nonlinear models estimate the resource allocation for multifunction radar search and track functions. Interactions between radars occur over time-invariant balanced graphs that may be directed or undirected. The collective search area and target-assignment solution for coordinated radars is optimized by balancing resource usage across the radar network and minimizing total resource usage. Agreement on the global optimal target-assignment solution is ensured using a distributed binary consensus algorithm. Monte Carlo simulations validate the coordinated approach over uncoordinated alternatives

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

A comprehensive survey on cultural algorithms

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