Joint transmitter selection and resource management strategy based on low probability of intercept optimization for distributed radar networks

In this paper, a joint transmitter selection and resource management (JTSRM) strategy based on low probability of intercept (LPI) is proposed for target tracking in distributed radar network system. The basis of the JTSRM strategy is to utilize the optimization technique to control transmitting resources of radar networks in order to improve the LPI performance, while guaranteeing a specified target tracking accuracy. The weighted intercept probability and transmit power of radar networks is defined and subsequently employed as the optimization criterion for the JTSRM strategy. The resulting optimization problem is to minimize the LPI performance criterion of radar networks by optimizing the revisit interval, dwell time, transmitter selection, and transmit power subject to a desired target tracking performance and some resource constraints. An efficient and fast three‐step solution technique is also developed to solve this problem. The presented mechanism implements the optimal working parameters based on the feedback information in the tracking recursion cycle in order to improve the LPI performance for radar networks. Numerical simulations are provided to verify the superior performance of the proposed JTSRM strategy

Autonomous agents for multi-function radar resource management

The multifunction radar, aided by advances in electronically steered phased array technology, is capable of supporting numerous, differing and potentially conflicting tasks. However, the full potential of the radar system is only realised through its ability to automatically manage and configure the finite resource it has available. This thesis details the novel application of agent systems to this multifunction radar resource management problem. Agent systems are computational societies where the synergy of local interactions between agents produces emergent, global desirable behaviour. In this thesis the measures and models which can be used to allocate radar resource is explored; this choice of objective function is crucial as it determines which attribute is allocated resource and consequently constitutes a description of the problem to be solved. A variety of task specific and information theoretic measures are derived and compared. It is shown that by utilising as wide a variety of measures and models as possible the radar’s multifunction capability is enhanced. An agent based radar resource manager is developed using the JADE Framework which is used to apply the sequential first price auction and continuous double auctions to the multifunction radar resource management problem. The application of the sequential first price auction leads to the development of the Sequential First Price Auction Resource Management algorithm from which numerous novel conclusions on radar resource management algorithm design are drawn. The application of the continuous double auction leads to the development of the Continuous Double Auction Parameter Selection (CDAPS) algorithm. The CDAPS algorithm improves the current state of the art by producing an improved allocation with low computational burden. The algorithm is shown to give worthwhile improvements in task performance over a conventional rule based approach for the tracking and surveillance functions as well as exhibiting graceful degradation and adaptation to a dynamic environment

Performance evaluation of track association and maintenance for a MFPAR with doppler velocity measurements

This study investigates the effects of incorporating Doppler velocity measurements directly into track association and maintenance parts for single and multiple target tracking unit in a multi function phased array radar (MFPAR). Since Doppler velocity is the major discriminant of clutter from a desired target, the measurement set has been expanded from range, azimuth and elevation angles to include Doppler velocity measurements. We have developed data association and maintenance part of a well known tracking method, Interacting Multiple Model Probabilistic Data Association

Tracking and control in multi-function radar

The phased array multi-function radar is an effective solution to the requirement for simultaneous surveillance and multiple target tracking. However, since it is performing the jobs usually undertaken by several dedicated radars its radar time and energy resources are limited. For this reason, and also due to the large cost of active phased array antennas, it is important for the strategies adopted in the control of the radar to be efficient. This thesis investigates and develops efficient strategies for multi-function radar control and tracking. Particularly the research has focused on the use of rotating array antennas and simultaneous multiple receive beam processing. The findings of the research challenge the traditional view that three or four fixed (static) array faces is the best antenna configuration for a multi-function radar system. By developing novel methods for the comparison of systems utilising different antenna configurations it is shown that a rotating array multi-function radar performs the surveillance function with a greater efficiency in its use of radar time than a static array system. Also, a rotating array system benefits from the ability to distribute the radar resources over the angular coverage in a way that is impossible with a static array system. A novel strategy is presented to achieve this, which allows the rotating array system to better support the realistic situation of a high concentration of radar tasks in a narrow angular sector. It is shown that the use of broadened transmit beams coupled with simultaneous multiple narrow receive beams can eliminate the compromise on radar beamwidth between the surveillance and tracking functions that is associated with multi-function radars. This technique would allow construction of multi-function radar systems with narrow beamwidths, giving improved tracking performance, without extending search frame times excessively. Efficient tracking strategies for both static array and rotating array multi-function radars are developed. They are applied through computer simulation to demonstrate tracking of highly manoeuvrable targets with a narrow beam multi-function radar. Track robustness is attained through the use of multiple beam track updating strategies at little cost in terms of radar time

Development of a Comprehensive Digital Avionics Curriculum for the Aeronautical Engineer

The purpose of this research was to develop a comprehensive digital avionics curriculum for aeronautical engineering students at AFIT. Due to the closing of the aeronautical engineering program at the Naval Postgraduate School, and the subsequent requirement to establish a digital avionics specialty course sequence at AFIT, a mature avionics curriculum does not yet exist that satisfies the needs of graduates who will serve as aeronautical engineers involved with the development, integration, testing, fielding, and supporting of military avionics systems as part of the overall aircraft system. Research was conducted through a comprehensive literature review and the use of a Delphi Technique survey process. 28 panel members representing the military, academe, and industry participated in a three round survey process that sought to identify the desired attributes of a newly graduated engineer and the specific subject areas of study that should be included within the avionics curriculum. The result of this research was the development of a proposed three course curriculum that will instill the desired attributes within the aeronautical engineers and provide them with the avionics knowledge required at the correct level of proficiency. Recommendations on how to implement the proposed curriculum in an effective and timely manner are presented

Concept definition for space station technology development experiments. Experiment definition, task 2

The second task of a study with the overall objective of providing a conceptual definition of the Technology Development Mission Experiments proposed by LaRC on space station is discussed. During this task, the information (goals, objectives, and experiment functional description) assembled on a previous task was translated into the actual experiment definition. Although still of a preliminary nature, aspects such as: environment, sensors, data acquisition, communications, handling, control telemetry requirements, crew activities, etc., were addressed. Sketches, diagrams, block diagrams, and timeline analyses of crew activities are included where appropriate

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges, given the complexity of interconnectivity and coordination required among the vehicles. To address this, multi-agent reinforcement learning (MARL), with its notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, has emerged as a promising tool for enhancing the capabilities of CAVs. However, there is a notable absence of current reviews on the state-of-the-art MARL algorithms in the context of CAVs. Therefore, this paper delivers a comprehensive review of the application of MARL techniques within the field of CAV control. The paper begins by introducing MARL, followed by a detailed explanation of its unique advantages in addressing complex mobility and traffic scenarios that involve multiple agents. It then presents a comprehensive survey of MARL applications on the extent of control dimensions for CAVs, covering critical and typical scenarios such as platooning control, lane-changing, and unsignalized intersections. In addition, the paper provides a comprehensive review of the prominent simulation platforms used to create reliable environments for training in MARL. Lastly, the paper examines the current challenges associated with deploying MARL within CAV control and outlines potential solutions that can effectively overcome these issues. Through this review, the study highlights the tremendous potential of MARL to enhance the performance and collaboration of CAV control in terms of safety, travel efficiency, and economy