Dynamic data driven sensor array fusion for target detection and classification

Target detection and classification using unattended ground sensors (UGS) has been addressed in literature. Various techniques have been proposed for target detection, but target classification is a challenging task to accomplish using the limited processing power on each sensor module. The major hindrance in using these sensors reliably is, that, the sensor observations are significantly affected by external conditions, which are referred to as context. When the context is slowly time-varying (e.g., day-night cycling and seasonal variations) the usage of the same classifier may not be a good way to perform target classification. In this paper, a new framework is proposed as a Dynamic Data Driven Application System (DDDAS) to dynamically extract and use the knowledge of context as feedback in order to adaptively choose the appropriate classifiers and thereby enhance the target classification performance. The features are extracted by symbolic dynamic filtering (SDF) from the time series of sensors in an array and spatio-temporal aggregation of these features represents the context. Then, a context evolution model is constructed as a deterministic finite state automata (DFSA) and, for every context state in this DFSA, an event classifier is trained to classify the targets. The proposed technique of detection and classification has been compared with a traditional method of training classifiers without using any contextual information.