Högskolan i Skövde

The protection of defended assets such as military bases and population centers against hostile targets (e.g., aircrafts, missiles, and rockets) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We review algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The reults show that many of the tested algorithms produce high quality solutions for small-scale problems. A novel vaiant of particle swarm optimization seeded with an enhanced greedy algorithm is described and is shown to perform best for large instances of the real-time allocation problem.

The problem of allocating defensive weapon resources to hostile targets is an optimization problem of high military relevance. The need for obtaining the solutions in real-time is often overlooked in existing literature. Moreover, there  does not exist much research aimed at comparing the performance of different  algorithms for weapon-target allocation. We have implemented a suite of  metaheuristic algorithms for solving the static weapon-target allocation problem, and compare their real-time performance on a large set of problem instances using the open source testbed SWARD. The compared metaheuristic algorithms are ant colony optimization, genetic algorithms, and particle swarm optimization. Additionally, we have compared the quality of the generated allocations to those generated by a well-known maximum marginal return algorithm. The results show that the metaheuristic algorithms perform well on small- and medium-scale problem sizes, but that real-time requirements limit their usefulness for large search spaces.

It is in military engagements the task of the air defense to protect valuable assets such as air bases from being destroyed by hostile aircrafts and missiles. In order to fulfill this mission, the defenders are equipped with sensors and firing units. To infer whether a target is hostile and threatening or not is far from a trivial task. This is dealt with in a threat evaluation process, in which the targets are ranked based upon their estimated level of threat posed to the defended assets. Once the degree of threat has been estimated, the problem of weapon allocation comes into the picture. Given that a number of threatening targets have been identified, the defenders need to decide on whether any firing units shall be allocated to the targets, and if so, which firing unit to engage which target. To complicate matters, the outcomes of such engagements are usually stochastic. Moreover, there are often tight time constraints on how fast the threat evaluation and weapon allocation processes need to be executed. There are already today a large number of threat evaluation and weapon allocation (TEWA) systems in use, i.e. decision support systems aiding military decision makers with the threat evaluation and weapon allocation processes. However, despite the critical role of such systems, it is not clear how to evaluate the performance of the systems and their algorithms. Hence, the work in thesis is focused on the development and evaluation of TEWA systems, and the algorithms for threat evaluation and weapon allocation being part of such systems. A number of algorithms for threat evaluation and static weapon allocation are suggested and implemented, and testbeds for facilitating the evaluation of these are developed. Experimental results show that the use of particle swarm optimization is suitable for real-time target-based weapon allocation in situations involving up to approximately ten targets and ten firing units, while it for larger problem sizes gives better results to make use of an enhanced greedy maximum marginal return algorithm, or a genetic algorithm seeded with the solution returned by the greedy algorithm.

The protection of defended assets such as military bases and population centers against ballistic weapons (e.g. rockets and mortars) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We propose algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The results show that a particle swarm optimization algorithm produce high quality solution for small-scale problems, and that agenetic algorithm yields the best solutions for the largest tested problem instances.

The allocation of firing units to hostile targets is an important process within the air defense domain. Many algorithms have been proposed for solving various weapon allocation problems, but evaluation of the performance of such algorithms is problematic, since it does not exist any standard scenarios on which to test the algorithms. It is to a large extent unknown how weapon allocation algorithms compare to each other when it comes to solution quality. We have developed the testbed SWARD, making it possible to systematically compare algorithm performance, and to support the development of new weapon allocation algorithms.

Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target's velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe an implemented testbed in which threat evaluation algorithms can be compared to each other, based on a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets). Our obtained results for two different threat evaluation algorithms are presented and analyzed.

The allocation of weapons to targets (such as missiles and hostile aircrafts) is a well-known resource allocation problem within the field of operations research. It has been proven that this problem, in general, is NP-complete. For this reason, optimal solutions to the static weapon-target allocation (WTA) problem can not be obtained in real-time for large-scale problems. We try to find the limit for how large problems that can be solved optimally in real-time by exhaustive search algorithms through running empirical experiments. We also propose a heuristic genetic algorithm for solving larger-scale problems.

In air defense situations, decision makers have to protect defended assets through assigning available firing units to threatening targets in real-time. To their help they have decision support systems known as threat evaluation and weapon allocation (TEWA) systems. The problem of performance evaluation of such systems is of great importance, due to their critical role. Despite this, research on this problem is close to non-existing. We are discussing the use of survivability and resource usage cost as comparative performance metrics, which can be used for comparing the effectiveness of different system configurations, by using simulations. These metrics have been implemented into a testbed, in which we have performed some comparative experiments. Our results show that changes of individual parts of the threat evaluation and weapon allocation system configuration can have a large effect on the effectiveness of the system as a whole, and illustrate how the metrics and the testbed can be used.

In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have been developed. The system is based on a Bayesian network approach, making it possible to handle imperfect observations. The structure of the Bayesian network is described in detail. Finally, an analysis of the system’s performance as applied to a synthetic scenario is presented.

 Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.