6,319 research outputs found
Cooperative Radar and Communications Signaling: The Estimation and Information Theory Odd Couple
We investigate cooperative radar and communications signaling. While each
system typically considers the other system a source of interference, by
considering the radar and communications operations to be a single joint
system, the performance of both systems can, under certain conditions, be
improved by the existence of the other. As an initial demonstration, we focus
on the radar as relay scenario and present an approach denoted multiuser
detection radar (MUDR). A novel joint estimation and information theoretic
bound formulation is constructed for a receiver that observes communications
and radar return in the same frequency allocation. The joint performance bound
is presented in terms of the communication rate and the estimation rate of the
system.Comment: 6 pages, 2 figures, to be presented at 2014 IEEE Radar Conferenc
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
Hybrid Cognition for Target Tracking in Cognitive Radar Networks
This work investigates online learning techniques for a cognitive radar
network utilizing feedback from a central coordinator. The available spectrum
is divided into channels, and each radar node must transmit in one channel per
time step. The network attempts to optimize radar tracking accuracy by learning
the optimal channel selection for spectrum sharing and radar performance. We
define optimal selection for such a network in relation to the radar
observation quality obtainable in a given channel. This is a difficult problem
since the network must seek the optimal assignment from nodes to channels,
rather than just seek the best overall channel. Since the presence of primary
users appears as interference, the approach also improves spectrum sharing
performance. In other words, maximizing radar performance also minimizes
interference to primary users. Each node is able to learn the quality of
several available channels through repeated sensing. We define hybrid cognition
as the condition where both the independent radar nodes as well as the central
coordinator are modeled as cognitive agents, with restrictions on the amount of
information that can be exchanged between the radars and the coordinator.
Importantly, each part of the network acts as an online learner, observing the
environment to inform future actions. We show that in interference-limited
spectrum, where the signal-to-interference-plus-noise ratio varies by channel
and over time for a target with fixed radar cross section, a cognitive radar
network is able to use information from the central coordinator in order to
reduce the amount of time necessary to learn the optimal channel selection. We
also show that even limited use of a central coordinator can eliminate
collisions, which occur when two nodes select the same channel.Comment: 34 pages, single-column, 10 figure
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects
Integrated Sensing and Communication (ISAC), combined with data-driven
approaches, has emerged as a highly significant field, garnering considerable
attention from academia and industry. Its potential to enable wide-scale
applications in the future sixth-generation (6G) networks has led to extensive
recent research efforts. Machine learning (ML) techniques, including
-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL)
architectures, and reinforcement learning (RL) algorithms, have been deployed
to address various design aspects of ISAC and its diverse applications.
Therefore, this paper aims to explore integrating various ML techniques into
ISAC systems, covering various applications. These applications span
intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs)
and autonomous cars, as well as radar applications, localization and tracking,
millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming.
The contributions of this paper lie in its comprehensive survey of ML-based
works in the ISAC domain and its identification of challenges and future
research directions. By synthesizing the existing knowledge and proposing new
research avenues, this survey serves as a valuable resource for researchers,
practitioners, and stakeholders involved in advancing the capabilities of ISAC
systems in the context of 6G networks.Comment: ISAC-ML surve
Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application
In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks
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