High precision 3-D coordinates for JSATS tagged fish in an acoustically noisy environment

Abstract

Abstract Background Acoustic tagging methods have been used to track fish for some time. Multiple systems have been developed, including those that give researchers the ability to position fish in three dimensions and time. However, proprietary positioning methods have suffered from a lack of transparency. The U.S. Department of Energy and the U.S. Army Corps of Engineers developed the Juvenile Salmon Acoustic Telemetry System (JSATS) to monitor the survivability of juvenile salmonids as they migrate downstream. With much smaller tags and high ping rates, JSATS positioning studies should be more prevalent, but implementation is difficult and often out of reach for small budget-minded studies. This study implemented a small scale JSATS positioning study using relatively inexpensive, autonomous, independent receivers. We will show that proper synchronization of the transmissions and elimination of multipath allows the positions of a smolt to be determined in three spatial dimensions over time with high precision. Results Tracking of 172 tagged smolts produced a total of nearly 2,00,000 positions. We compared the performance of four different supervised machine learning classifiers (Support Vector Classifier (SVC), Gaussian Naïve Bayes (NB), Classification Tree (CART), and K-Nearest Neighbor (KNN). All algorithms performed well with high accuracy and precision, but recall rates decreased with distance from the source. The SVC and KNN were least restrictive in practice. Overall, the SVC had the longest time to solve. Conclusion Positions determined from fish outside of the convex hull of the hydrophones were effectively being extrapolated, while positions determined from within the convex hull nearly always met or exceeded 1-m precision. Having stationary submerged hydrophones was necessary to produce three-dimensional positions. The main technical advances presented are the hydrophone-clock synchronization scheme and the multipath rejection scheme, which found the best multipath classifier to be the K-Nearest Neighbor. Neither algorithm was capable of alleviating close proximity detection interference (CPDI), suggesting the need to reposition receivers from reflective surfaces or install baffling.