Intelligent active sonar via tuning of transmit waveform and detection threshold

We consider active sonar systems that translate high-level tasks to a set of parameter adaptations and arbitrate among competing demands, thereby incorporating cognitive processing in the system. We propose adopting the goal-driven autonomy (GDA) architecture presented in Klenk et al. [Computational Intelligence, May 2013] to realize intelligent active processing. Using the GDA architecture, the sonar system uses its observations to inform how system parameters are tuned to achieve a set of surveillance goals. In addition, the system is able to reason about its actions, identifying discrepancies between predicted and observed performance and modifying both parameters and goals accordingly. In this work, we focus on a tracking task and consider the transmit waveform and detection threshold as tunable parameters. We describe an active sonar system that tunes these two parameters based on observations of the physical environment and target characteristics, as well as current goals. The intelligent system uses its observations of the environment to generate estimates of target Doppler and of clutter density to tune waveform and detection threshold, respectively. Through simulation, we demonstrate the performance improvement achieved by the cognitive system when tracking a maneuvering target that is traveling through spatially non-uniform clutter.