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How to utilize the limited power budget to accurately track more targets plays a critical role for the radar system in air defense applications, especially in the weapon guidance application. In this paper, we propose a robust power allocation (RPA) strategy in the collocated multiple-input and multiple-output (C-MIMO) radar system for multiple target guidance (MTG) under blanket jamming. The optimization model is established with the aim of increasing the number of effective tracking targets (ETTs) and improving the overall tracking accuracy among those targets. Since the mutual information (MI) quantifies the parameter estimation performance and can be predicted, the MI under blanket jamming is derived and utilized as the optimization criterion. We then propose a two-step optimization algorithm based on benefit–cost ratio (BCR) to solve the non-convex problem. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new bi-objective model for dynamic weapon-target assignment problem. We consider that the initial weapon assignment plan of defense is disrupted during engagement because of a destroyed air target, breakdown of a weapon system or a new incoming air target. The objective functions are defined as the maximization of probability of no-leaker and the maximization of stability in engagement order of weapon systems. Stability is defined as assigning same air target in sequence in engagement order of a weapon system so that reacquisition and re-tracking of air target are not required by sensors. We propose a new solution procedure to generate updated assignment plans by maximizing efficiency of defense while maximizing stability through swapping weapon engagement orders. The proposed solution procedure generates non-dominated solutions from which defense can quickly choose the most-favored course of action. We solve a set of representative problems with different sizes and present computational results to evaluate effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the continuous advancement of science and technology, the social network based on the Internet has gradually penetrated into people's daily lives. The image data shared on social media using platforms such as mobile phones has exploded, and hundreds of millions of dollars are generated every day with picture information. In the past, the traditional ways of expressing textual information that people are familiar with have begun to be gradually replaced by image information that is not subject to regional culture such as language and script. In modern warfare, with the continuous development and equipment of highly sophisticated weapons and equipment, the amount of information that the entire combat system needs to process will also increase. In particular, the air defense system needs to quickly and accurately identify the aircraft targets that are coming. It mainly uses computer to extract the feature information of the acquired image and converts the content in the image into a feature expression that can be processed by the computer. After the appropriate classification algorithm, the image is the target object is classified by category. In this paper, we propose a complex image recognition algorithm based on immune random forest model. The experimental results show that the proposed algorithm has high recognition efficiency and higher robustness. [ABSTRACT FROM AUTHOR]

This paper examines how South Korea might be able to deter the growing threat from North Korea’s nuclear weapons and missile programs. North Korea’s six nuclear weapons experiments with various types of missile test fires using solid fuel and new technology pose a security threat to South Korea and the USA. The credibility of the North’s WMD capability and the military balance of two Koreas are critically analyzed. South Korea’s superior conventional weapons system, supported by U.S. strategic weapons and intelligence systems, could fend off North Korean nuclear weapons and missile attacks. However, it is imperative that the U.S.-ROK alliance continues to develop a stringent nuclear deterrence strategy and deploy a series of countermeasures, including preemptive strikes and a multilayered missile defense system, such as Kill Chain, KAMD, and THAAD, all to guard against a potential nuclear weapon attack from North Korea. [ABSTRACT FROM AUTHOR]

This paper applies a distributed theory of situation awareness based upon the analysis of interactions between agents (both human and non-human) in an Airborne Warning and Control System (Boeing E3D Sentry). The basic tenet of this approach is that agents within a system each hold their own component(s) of situation awareness, which may be very different from, but compatible with, other agent’s view of the situation. However, it is argued that it is not always necessary to have complete sharing of this awareness, as different system agents have different purposes. Situation awareness is regarded as a dynamic and collaborative process that binds agents together on tasks on a moment-by-moment basis. Situation awareness is conceptualised as residing at a system, not an individual level. Data were collected from crew-members in theE3D during a series of simulated air battles. These data pertained to task structure, communications between the crew and the collection and analysis of crew actions at critical decision points. All phases of operations were considered. From these data propositional networks were developed in which key knowledge objects were identified. Analysis of these networks clearly shows how the location and nature of distributed situation awareness changes across agents with regard to the phase of operation/air battle. [ABSTRACT FROM AUTHOR]

This study focuses on the optimal deployment and positioning of missile systems to protect a region against possible threats using the layered air defense system. A multi-objective optimization model is proposed to maximize the probability of killing and minimize the total cost. Unlike previous studies, our study takes into account different types of missiles. Our goal is to provide a set of solutions for potential future threat scenarios. The multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) model is used as a heuristic algorithm, and the model is implemented in MATLAB. We then perform a comparative analysis using a goal programming (GP) model. The GP model is solved using the Generalized Reduced Gradient (GRG) method with the Excel Solver tool. The results of the comparative analysis show that the MOFEPSO model has some advantages over the GRG solution in terms of sensitive results related to the probability of kill and the diversification of missile investment costs. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem, which has received much attention in the literature and is of continuing relevance, seeks within an air defense context to assign interceptors (weapons) to incoming missiles (targets) to maximize the probability of destroying the missiles. Kline et al. (J Heuristics 25:1–21, 2018) developed a heuristic algorithm based upon the solution to the Quiz Problem to solve the WTA. This heuristic found solutions within 6% of optimal, on average, for smaller problem instances and, when compared to a leading WTA heuristic from the literature, identified superlative solutions for larger instances within hundredths of a second, in lieu of minutes or hours of computational effort. Herein, we propose and test an improvement to the aforementioned heuristic, wherein a modified implementation iteratively blocks exiting assignments to an initial feasible solution, allowing superior solutions that would otherwise be prevented via a greedy selection process to be found. We compare these results to the optimal solutions as reported by a leading global optimization solver (i.e., BARON) and find solutions that are, at worst, within 2% of optimality and, at best, up to 64% better than the solutions reported to be optimal by BARON. To wit, the developed metaheuristic outperformed BARON in 25% of all instances tested, as BARON reported a suboptimal solution as being optimal for 21.1% of the instances, and it could not identify an optimal solution for the remaining 6.67% of the instances within 2 h of CPU time, a liberally imposed time limit that far exceeds practical usage considerations for this application. [ABSTRACT FROM AUTHOR]

In this study, a decision support system (DSS) based on the interactive use of location models and geographical information systems (GIS) was developed to determine the optimal positions for air defence weapons and radars. In the location model, the fire units are considered as the facilities to be located and the possible approach routes of air vehicles are treated as demand points. Considering the probability that fire by the units will miss the targets, the objective of the problem is to determine the positions that provide coverage of the approach routes of the maximum number of weapons while considering the military principles regarding the tactical use and deployment of units. In comparison with the conventional method, the proposed methodology presents a more reliable, faster, and more efficient solution. On the other hand, owing to the DSS, a battery commander who is responsible for air defence becomes capable of determining the optimal weapon and radar positions, among the alternative ones he has identified, that cover the possible approach routes maximally. Additionally, he attains the capability of making such decisions in a very short time without going to the field over which he will perform the defence and hence without being subject to enemy threats. In the decision support system, the digital elevation model is analysed using Map Objects 2.0, the mathematical model is solved using LINGO 4.0 optimization software, and the user interface and data transfer are supported by Visual Basic 6.0. [ABSTRACT FROM AUTHOR]