Modern sensors, such as multifunctional radars, comprise many settings and the number of controllable settings is increasing due to technological advance. Although having many settings allows many capabilities, it simultaneously requires an automatic manager to control them. This paper proposes mission-driven resource management to control reconfigurable sensing systems during run time. It works as follows. First, expected mission success is defined from an end-user point of view. Because such criterion can eventually be mathematically linked to the adaptable parameters of the reconfigurable systems, these parameters can be optimally selected. To illustrate, a case study is considered where many heterogeneous high-level operational tasks (i.e., air defense, weather alarm, crowd control, and drone flight) have to be supported by several fully reconfigurable radio-frequency antenna front ends. This paper analytically and numerically compares the proposed mission-driven method that maximizes end-user’s expectations for mission success, with a traditional task-driven one, that solely optimizes task performance characteristics. To conclude, by maximizing expected mission success, the systems are automatically adapted to new circumstances, and the end-user’s mission is constantly supported by the system in the most effective way.

This paper proposes feature extraction methods for object classification with passive acoustic sensor networks deployed in suburban environments. We analyzed the emitted acoustic signals of three object classes: 1) guns (muzzle blast); 2) vehicles (running piston engine); and 3) pedestrians (several footsteps). Based on the conducted analysis, methods are developed
to extract the features that are related to the physical nature of the objects. In addition, a time-based location method is
developed (based on a pseudo-matched-filter), because the object location is required for one of the feature extraction methods.
As a result, we developed a proof-of-concept system to record and extract discriminative acoustic features. The performance
of the features and the final classification are assessed with real measured data of the three object classes within suburban
environment.