Classification of uncooperative vehicles with sparse laser Doppler vibrometry measurements

Abstract

Recently Laser Doppler Vibrometry (LDV) has been widely employed to achieve long-range sensing in military applications, due to its high spatial and spectral resolutions in vibration measurements that facilitates effective analysis using signal processing and machine learning techniques. Based on the collaboration of The City College of New York and the Air Force Research Laboratory in the last several years, we have developed a bank of algorithms to classify different types of vehicles, such as sedans, vans, pickups, motor-cycles and buses, and identify various kinds of engines, such as Inline-4, V6, 1- and 2-axle truck engines. Thanks to the similarities of the LDV signals to acoustic and other time-series signals, a large of body of existing approaches in literature has been employed, such as speech coding, time series representation, Fourier analysis, pyramid analysis, support vector machine, random forest, neural network, and deep learning algorithms. We have found that the classification results based on some of these methods are extremely promising. For instance, our vehicle engine classification algorithm based on the pyramid Fourier analysis of the engine vibration and fundamental frequencies of vehicle surfaces over the data collected by our LDV in the summer of 2014 have consistently attained 96% precision. In laboratory studies or well-controlled environments, a great array of high quality LDV measured points all over the vehicles are permitted by the vehicle owners, therefore extensive classifier training can be conducted to effectively capture the innate properties of surfaces in the space and spectral domains. However, in real contested environments, which are of utmost interest and practical importance to military applications, the uncooperative vehicles are either fast moving or purposively concealed and thus not many high quality LDV measurements can be made. In this work an intensive study is performed to compare the performance in vehicle classifications under the cooperative and uncooperative environments via LDV measurements based on a content-based indexing approach. The method uses an iterative Fourier analysis and an artificial feed-forward neural network. As our empirical studies have suggested, even in uncooperative and contested environments, with adequate training dataset for similar vehicles, our classification approach can still yield promising recognition rates.