# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Acoustic-Based Detection
#### 2.2.1 Traditional Acoustic-Based Detection  
Cepstrum analysis is a widely used technique for analyzing audio signals, and it entails computing the inverse Fourier transform of the logarithmic power spectrum values. Many feature extraction methods for audio signals are developed based on cepstrum coefficients. The commonly used cepstrum coefficients include Linear Predictive Cepstrum Coefficient (LPCC) and Mel Frequency Cepstrum Coefficient (MFCC). By applying these techniques to audio-based UAV detection, researchers have achieved promising results in identifying UAVs based on their unique acoustic features.  
The Mel frequency is a non-linear frequency scale that was designed to model the way in which the human ear perceives sound. It does not have a linear correspondence with Hz frequency. The MFCC is a spectrum feature that is computed based on the Mel frequency scale, and it has found extensive use in the domain of speech recognition. However, the accuracy of MFCC calculation decreases with increasing frequency. As a result, in practical applications, only low-frequency MFCCs are typically used, while medium to high frequency MFCCs are discarded. Despite this limitation, MFCC remains the most widely employed audio feature in current UAV recognition tasks, as it has demonstrated effectiveness in identifying UAVs based on their unique acoustic features [38; 52; 53; 54; 55]. The accuracy when using MFCC can be over 97%.The audio signal is initially segmented into frames and windowed. This involves dividing the signal into shorter time intervals and applying a window function to each interval to minimize spectral leakage. Each frame undergoes the application of the Fast Fourier Transform (FFT) to derive its linear spectrum. The linear spectrum is subsequently filtered using a bank of Mel filters that are spaced according to the Mel frequency scale. The logarithm of the filtered spectrum is then computed to obtain the LogMel spectrogram. The LogMel spectrogram is subsequently subjected to the Discrete Cosine Transform (DCT), an instrumental method utilized for the transformation of spectral data from the time domain to the frequency domain. A desired number of DCT coefficients are then retained to obtain the MFCC, which can be used as a feature for audio-based UAV detection.  
Linear Predictive Cepstral Coefficients (LPCC) is a commonly used method for feature extraction in audio signal analysis [56] and is a variant of MFCC. Similar to MFCC, LPCC extracts features by preprocessing, framing, filter bank processing, Mel cepstrum, DCT, and other steps on speech signals. However, LPCC uses linear prediction (LP) filters instead of triangular filter banks in the filter bank processing stage to extract resonance peak information of speech signals. The LP filters used by LPCC in filter bank processing can more accurately extract the formant information of speech signals, making LPCC more effective than MFCC in the presence of Gaussian white noise. However, the high complexity of the LP filters and the corresponding increase in computational complexity must be balanced in practical applications. In the literature, LPCC is usually used as a supplement to MFCC, and the amalgamation of these two features is commonly employed to bolster the performance of UAV recognition [53].