无人机活动概率和反制落点预判算法

doi:10.3778/j.issn.1002-8331.2405-0166

摘要/Abstract

摘要： 针对无人机探测中出现遗漏或误警率过高，以及需要对“黑飞”无人机提前预判反制后落点的问题，提出了一种评估无人机活动概率和预判反制落点的算法。基于朴素贝叶斯模型，设计了一种通过区域内天气大数据和区域内出现无人机概率的历史大数据计算无人机活动概率的算法。针对无人机遭反制后以平抛运动态势坠落的情景，在考虑风力、空气阻力等因素下，设计出预判落点位置和落地动能的算法。把复杂低空环境下可能影响落点位置的空气温度、湿度和气压作为次要因素，基于朴素贝叶斯算法计算落点偏移概率。提出根据朴素贝叶斯算法与Sigmoid函数相结合的方法，确定落点位于某区域范围内概率的算法。通过理论仿真和外场试验的结果对比，验证了反制落点预判算法的可信度。

关键词: 无人机, 探测, 活动概率, 反制, 预判

Abstract: An algorithm is proposed to assess the probability of drone activity as well as to predict the landing point of drone after countermeasure, particularly aimed at addressing issues such as missed detections, high false alarm rates, and the need for early prediction of the landing point of “rogue” drone. Based on the Naive Bayes model, an algorithm is designed to calculate the probability of drone activity using big data on local weather and historical probabilities of drone appearances in the area. For drone falling in a projectile motion after being countered, an algorithm is developed to predict the landing point position and ground kinetic energy considering factors such as wind speed and air resistance. The complex low-altitude environmental factors that can affect the landing position, such as air temperature, humidity, and pressure, is considered as secondary factors. The Naive Bayes algorithm is used to calculate the probability of landing points deviation. A method combining the Naive Bayes algorithm with the Sigmoid function is proposed to determine the probability that the landing points falls within a certain area. By comparing the results of theoretical simulation and field experiments, the credibility of the prediction algorithm for countermeasure landing points is verified.

Key words: drone, detection, activity probability, countermeasure, prediction

张宏展, 陈鹏, 陈勇. 无人机活动概率和反制落点预判算法[J]. 计算机工程与应用, 2025, 61(6): 164-170.

ZHANG Hongzhan, CHEN Peng, CHEN Yong. Probability of Drone Activity and Countermeasure Landing Point Prediction Algorithm[J]. Computer Engineering and Applications, 2025, 61(6): 164-170.

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