Long Range Acoustic Classification

This paper introduces the use of dynamic features for robust target recognition of ground vehicles. Most current approaches rely on instantaneous spectral features such as those derived from harmonically related spectral lines. Significant drawback of these approaches are that the use of low amplitude (10-20dB below dominant line) spectral lines severely limit classification range. The strongest line is often detectable well before secondary lines. Dynamic features extracted directly from the strongest spectral line, if successfully characterizing the target, will extend the range of operation to several times. In this report, a complete experimental evaluation of the effectiveness of dynamic features is conducted. The analysis is performed using a database consisting of approximately two hundred acoustic signatures collected from six unique vehicles. A number of features captured from the dynamic characteristic of the spectral line are evaluated. Classification performance is measured and presented in terms of confusion matrices. As an additional test of the classifier development tools developed for this task, we selected added instantaneous spectral measurements to the dynamic feature, and re-tested. We found that the performance of the classifiers using the mixed spectral and dynamic features was excellent, but “blind” testing of the classifiers that were developed (testing against vehicle runs that were not used during classifier development) showed disappointing results. Introduction The primary challenge for the success of ground vehicle classification using acoustic signature is in the area of searching for robust features for class recognition. In the past, feature design has been primarily driven by the fundamental physics of the engine mechanics, which translates acoustic energy into series of narrow band spectral peaks. These harmonically related signal components are directly related to the engine firing rate and track slap. It is then natural to classify vehicles using the feature that relate to the makeup of these harmonic lines usually detected by Harmonic Line Association (HLA) algorithm. One difficulty these techniques encounter is the low probability of detection of secondary spectral lines. It has been shown that the acoustic signature of ground vehicles is nonstationary due to many factors. Some of these dynamics are believed to be from the engine itself and some from the influence of environments such as the terrain, atmosphere and geologic characteristics. In this paper, we investigate means to extract features from the dynamic aspects of signals. The application of dynamic features in classification is motivated by the recent success of many speech recognition algorithms. Our primary objective is to evaluate classification effectiveness of transient/dynamic features that could be computed from tracking a single spectral line. If successful, it will extend the tactically useful ranges for ground vehicles several times. We used the ARL ACIDS database and a multi-variate classifier (MVG) to quantitatively evaluate our features. Figure 1 Figure 2 Approved for public release; distribution is unlimited.