A compensated sliding-window DFT algorithm for fine-grained underwater acoustic ranging

Fine-grained (sub-meter) ranging and localization is critical to the deployment of dense, mobile sensor networks in aquatic environments. However, such a task is faced with a number of challenges, including noisy underwater environments, limitation on size and complexity of localization hardware, and constraints on computing capabilities of sensor platforms. In this paper we present a sliding-window discrete Fourier transform (DFT)-based algorithm for precise detection of the arrival of a monotone acoustic signal, as a key enabling step in measuring the time of flight (TOF) of the acoustic signal for localization of the sensor node. The algorithm accommodates the rise dynamics of the signal and compensates for the latency in detection given the signal model, detection threshold, and steady-state signal amplitude. The algorithm is implemented onboard a small biomimetic robotic fish, and experiments in an indoor pool have shown that the proposed method results in an underwater ranging error of 1.4 wavelengths (74.3 cm), and is thus promising for localization of dense aquatic networks. The proposed method has also shown robustness in comparison with other tested methods including a matched filter-type method.