Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model
Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model
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DOI：
10.1177/1351010x20917856
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发表时间：
2020-05-13
影响因子：
1.7
通讯作者：
Trematerra, Amelia
中科院分区：
文献类型：
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作者：
Iannace, Gino;Ciaburro, Giuseppe;Trematerra, Amelia
在这项研究中，从声学测量获得的数据被用来训练基于逻辑回归的模型，以检测室内环境中的四旋翼飞行器。为了模拟一个真实的环境，我们在一个购物中心做了录音。记录了与两种情况有关的声音：只有人择噪声和有背景音乐的人择噪声。后来，我们在与购物中心相同大小和特征的室内环境中重现了这些声音。在模拟测试过程中，放置在距离声级计不同距离处的无人机以不同速度开启，以识别它们在复杂声学场景中的存在。随后，这些测量被用来实现基于逻辑回归的模型，用于无人驾驶飞行器的自动检测。Logistic回归广泛应用于二元因变量的模式识别。该模型返回高精度值（0.994），表明正确检测的数量很高。在这项研究中获得的结果表明，使用这种工具的无人驾驶航空器检测应用。
In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor's vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.