Remote Vibrometry Vehicle Classification

In vehicle target classification, contact sensors have frequently been used to collect data to simulate laser vibrometry data. Accelerometer data has been used in numerous literature to test and train classifiers instead of laser vibrometry data ([1] [2]). Understanding the key similarities and differences between accelerometer and laser vibrometry data is essential to keep progressing aided vehicle recognition systems. This paper investigates the contrast of accelerometer and laser vibrometer data on classification performance. Research was performed using the end-to-end process previously published by the authors to understand the effects of different types of data on the classification results. The end-to-end process includes preprocessing the data, extracting features from various signal processing literature, using feature selection to determine the most relevant features used in the process, and finally classifying and identifying the vehicles. Three data sets were analyzed, including one collection on military vehicles and two recent collections on civilian vehicles. Experiments demonstrated include: (1) training the classifiers using accelerometer data and testing on laser vibrometer data, (2) combining the data and classifying the vehicle, and (3) different repetitions of these tests with different vehicle states such as idle or revving and varying stationary revolutions per minute (rpm).