# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Acoustic-Based Detection
#### 2.2.2 Acoustic-Based Detection Using Deep Learning  
Acoustic-based UAV detection has two primary directions of research. The first direction involves accurately and quickly separating sound sources and identifying UAV acoustic signals in noisy environments, which can be achieved using techniques such as ICA and fastICA [57]. The second direction focuses on exploring UAV detection in time-varying scenarios, which is important due to the rapid movement of UAVs and theneed to track their acoustic signatures over time [57]. While acoustic-based UAV detection is less commonly used in commercial and military anti-UAV systems due to its short detection range, this approach still has significant potential for application in urban IoT environments such as residential areas, schools, and commercial areas. By leveraging the unique acoustic signatures of UAVs, this approach can help to identify and track UAVs in real-time, providing valuable information for UAV detection and countermeasure systems. Future research in this area may focus on developing more accurate and robust acoustic-based UAV detection methods that can function across a range of acoustic environments and under various operating conditions of UAVs.  
Independent Component Analysis (ICA) is a computational method that can be employed to decompose multivariate signals into additive subcomponents. This is done under the assumption that the subcomponents are non-Gaussian and statistically independent of one another. ICA is a special case of blind source separation, and it can be used to separate acoustic signals from mixed recordings. ICA's ability to separate signals that are mixed with strong interference sources makes it a promising technique for detecting single or multiple UAVs when the signals are contaminated with noise [57]. In practice, ICA can be used as a part of signal preprocessing and combined with other analysis methods. For example, in Refs. [53; 57], a combination of ICA and MFCC is used for UAV identification, with ICA used to preprocess the audio signals and extract independent acoustic sources before applying MFCC for feature extraction.  
In addition to traditional signal processing techniques, learning-based methods are also widely used for feature extraction in audio-based UAV detection. Recent research [38; 39] has shown that deep learning models can also be used to extract features from UAV acoustic signals for UAV detection. In Ref. [27], the authors converted audio clips into spectrograms and used them as input for deep learning algorithms. To train deep learning models, the algorithm extracted multiple features from the spectrograms generated by the audio signals. In Ref. [39], the received audio signal was converted into the MEL spectrum by short-term Fourier transform, recorded as an image, and then input into a specially designed Light-Weight Convolutional Neural Network to extract signal features. In Ref. [38], Log Mel spectrograms and MFCC were used as inputs to CNN to classify the acoustic signals as either indicative of UAV activity or not. Table 5 presents the accuracy of these methods. It showcases the effectiveness of the approaches described earlier in detecting UAVs using acoustic signals.