KEYWORDS: Acoustics, Sensors, Doppler effect, Time-frequency analysis, Signal to noise ratio, Signal detection, Interference (communication), Data modeling, Statistical analysis, Signal processing
This paper presents analysis on the ability to classify fixed-wing aircraft based on their acoustic signatures. Since only a small amount of data was available, the paper focuses on feature extraction. We analyzed a data set for a single propellor and a single jet aircraft. Both spectral and cepstral analyses were performed on the data. Both nonparametric and parametric methods were used to estimate the power spectrum. For the propellor aircraft, the frequency ratio between spectral lines was found to be a useful feature for classification. The cepstrum of both the propellor and jet aircraft acoustic data were found to contain features related to engine rotation rates.
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