weapon-targetassignment is a multi-agent control problem in which each weapon is assigned to a target to minimize the expected survival value of the targets. In this work, a multi-objective version of the weapon-targ...
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weapon-targetassignment is a multi-agent control problem in which each weapon is assigned to a target to minimize the expected survival value of the targets. In this work, a multi-objective version of the weapon-targetassignment problem is considered in which the quality of an assignment is dependent on both the total effectiveness of the weapons assigned to each target and the relative timing of agents' arrival. Such timing constraints may be important in real-world scenarios in which a mission planner wishes to enforce an element of surprise on each target. Building on previous work, a new modified cost function is presented that couples weapon effectiveness and timing metrics into a combined cost. In cases where weapon-target closing speeds are limited to a certain range, this combined cost allows the inclusion of arrival time constraints in the assignment decision process. The performance of this new cost function is demonstrated through theoretical analysis and simulation. Results show that the proposed cost function balances the dual goals of optimizing effectiveness and arrival time considerations under closing speed limitations and that a user-defined tuning parameter can be used to adjust the priority of the dual goals of sequenced arrival and achieving the desired probability of kill.
The object population in the space around the Earth is subject to increase. With the advancements in sensor capabilities, it can be expected that, at the same time, more of those objects will be detected. Although thi...
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The object population in the space around the Earth is subject to increase. With the advancements in sensor capabilities, it can be expected that, at the same time, more of those objects will be detected. Although this allows for significant advances in understanding of the detectable objects and the expansion of object catalogs, it also leads to significant stress on the sensor systems and makes efficient sensor tasking a prime challenge. To solve sensor tasking as an optimization problem of observing objects when a priori information is available, various methods exist. Classical methods rely on the problem being formulated in a convex representation. Computationally intensive methods based on artificial intelligence, such as machine learning, have recently gained a lot of attention and are suitable for problems even when no convex formulation can be found. In this paper, performances of a simple traditional greedy algorithm and the more complex weapon-targetassignmentalgorithm are compared with the performance of two machine learning algorithms: ant colony and distributed Q-learning. Ant colony optimization is a swarm optimization path finding methodology based on probabilistic principles;distributed Q-learning aims to find an optimal policy by maximizing the expected reward received. As an application case the observation of known objects in the geosynchronous region with a ground-based sensor is used, and performance is evaluated in terms of the number of objects successfully tracked, the computational efficiency of running the algorithms, and the difficulty of tuning the algorithms. The ant colony solutions track the most objects, whereas the greedy algorithm is the most efficient;additionally, the ant colony and distributed Q-learning require significant tuning of the algorithms before employment.
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