We present an innovative approach to quickly estimate collateral damage based on the given weapon-target assignment (WTA). That is, having the predetermined WTA, we estimate the collateral damage of friendly or neutral entities based on the lethality of engaging weapons and the geo-locations of all the entities in the theater. Specifically, we calculate the probability of killing/destroying k friendly (or neutral) assets for given k. The main motivation of this paper is twofold. First, quick collateral damage estimation (QCDE) can support commanders in the battlefields with a new quick decision-making capability. Second, our QCDE could support WTA capabilities with collateral damage consideration. Our computational results indicate that the error in estimating collateral damage is significantly less than 1% and that the execution times are of the order of milliseconds for k (k <;9) assets. Hence, the natural research challenge related to this paper would be the extension of our quick collateral damage estimation to k ≥ 9 that would execute in reasonable time and give a reasonable quality solution.

In this paper, we present a novel optimization algorithm for assigning weapons to targets based on desired kill probabilities. For the given weapons, targets, and desired kill probabilities, our optimization algorithm assigns weapons to targets that satisfy the desired kill probabilities and minimize the overkill. The minimization of overkill assures that any proper subset of the weapons assigned to a target results in a kill probability that is less than the desired kill probability on such a target. Computational results for up to 120 weapons and 120 targets indicate that the performance of this algorithm yields an average improvement in quality of solutions of 26.8% over the greedy algorithms, whereas execution times remained on the order of milliseconds.

Effect-based weapon-target pairing assigns weapons to targets for the given desired effects on such targets. The most obvious and natural effects on targets are represented by the percentages of damage of these targets. In this paper, we focus on the generation of input for effect-based weapon-target pairing optimization. One way to generate such input is based on the Joint Munition Effectiveness Manual (JMEM). JMEM allows the evaluation of the weapons. It is a database that contains many tables, and each table contains many different data fields. Because of the sheer size of JMEM, the optimization of weapon-target pairing based on JMEM is currently focused mainly on one target at a time. In other words, the optimization of weapon-target pairing for many targets and weapons is not directly supported by JMEM, although all the necessary data is there. In this paper, we derive an input based on the given JMEM and desired effect(s), which should be useful in the follow-on effect-based weapon-target pairing optimization that is not limited to a single weapon or target. In particular, effect-based weapon-target pairing will rely on the scanning of the attack guidance table that we derive from JMEM to determine a preferred set of weapon combinations for engaging a given set of targets.