Situational awareness in a Persistent Surveillance System (PSS) can be significantly improved by fusion of Data from physical (Hard) sensors and information provided by human observers (as Soft/biological sensors) from the field. One of the major limitations that this trend brings about is, however, the integration and fusion of the sensory data collected from hard sensors along with soft data gathered from human agents in a consistent and cohesive way. This paper presents a proposed approach for semantic labeling of vehicular non-stationary acoustic events in the context of PSS. Two techniques for feature extraction based on discrete wavelet and short-time Fourier transforms are described. A correlation-based classifier is proposed for classifying and semantic labeling of vehicular acoustic events. The presented result demonstrates the proposed solution is both reliable and effective, and can be extended to future PSS applications.