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Assessing Resource Requirements for Maritime Domain Awareness and Protection (Security)

机译:评估海事领域感知和保护(安全)的资源要求

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摘要

An allied (Blue) maritime domain contains a number w (w1) of non-hostile neutral W (White) vessels. A hostile R (Red) vessel enters the domain. R is traveling through the domain towards a target. Overhead allied (Blue) sensors: manned aircraft, helicopters, and/or unmanned aerial sensor vehicles (UAVs), patrol the domain and classify (perhaps incorrectly) detected vessels as R or W. The misclassification of a W as an R is a false positive. An overhead sensor follows (or tracks) any vessel classified as R until it is relieved by another platform, e.g. a destroyer pair (DD). The overhead sensor is unable to detect and classify additional vessels while it is following a suspicious vessel. This may well be a somewhat pessimistic assumption. Models are formulated and studied to evaluate the probability that R is successfully neutralized before reaching its destination. The model results quantify the effect of the resources and time needed to prosecute misclassified neutral vessels (false positives) on the probability of successfully neutralizing R. The probability of neutralizing R depends on the area of the domain being patrolled, the number of sensor platforms, the sensor platform velocity, the time to classify a vessel of interest, the ability to correctly classify vessels of interest, the time until a sensor platform following a suspicious vessel is relieved, and the false positive rate. The results indicate that the probability of neutralizing an R vessel is very sensitive to the false positive rate. Technologies, processes, and procedures that can decrease the false positive rate will increase the effectiveness of the Maritime Intercept Operation (MIO). The same is true also of false negatives: classifying the R as a W. Note that we do not investigate the effect of tagging or labeling a detected entity; this has a down side if tagging is too error-prone. This important and interesting investigation is postponed.
机译:一个联盟的(蓝色)海洋域包含数量为w(w 1)的非敌对中立W(白色)船只。敌对的R(红色)船只进入域。 R正在通过域向目标移动。架空同盟(蓝色)传感器:有人驾驶飞机,直升机和/或无人空中传感器车辆(UAV),在该域中巡逻,并将检测到的船只分类为R或W(可能错误地)。将W误分类为R是错误的正。高架传感器跟踪(或跟踪)归类为R的任何船只,直到由另一个平台将其释放为止。一对驱逐舰(DD)。高架传感器在跟踪可疑船只时无法检测和分类其他船只。这可能是一个有点悲观的假设。制定模型并进行研究,以评估R到达目的地之前成功中和的可能性。模型结果量化了起诉错误分类的中性血管(假阳性)所需的资源和时间对成功中和R的可能性的影响。中和R的可能性取决于巡逻域的面积,传感器平台的数量,传感器平台的速度,对感兴趣的血管进行分类的时间,对感兴趣的血管进行正确分类的能力,直到可疑血管之后的传感器平台被释放的时间以及假阳性率。结果表明,中和R容器的可能性对假阳性率非常敏感。可以降低误报率的技术,过程和程序将提高海上拦截行动(MIO)的有效性。否定否定也是如此:将R归类为W。请注意,我们不调查标记或标记检测到的实体的效果;如果标记太容易出错,这将不利。这项重要而有趣的调查被推迟了。

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