We consider the challenge of tracking and estimating the size of a single submerged target in a high reverberant underwater environment using a single active acoustic transceiver. This problem is common for a multitude of applications, ranging from the security and safety needs of tracking submerged vehicles and scuba divers, to environmental research and management implications such as the monitoring of pelagic fauna. Considering that the target can be either slow (e.g., a scuba diver) or fast moving (e.g., a shark), we avoid continuous signaling, and rely on the emission of wideband pulses whose reflection pattern are evaluated and reshaped in a time-distance matrix. As opposed to common approaches that track targets through template matching or by using tracking filters, we avoid making difficult assumptions about the target's reflection patterns or motion type, and instead perform probabilistic tracking using a constraint Viterbi algorithm, whereby detection is determined based on maximum likelihood criterion. In this process, we use the expectation-maximization approach to manage stationary reflections through distribution analysis, which otherwise may be misidentified as targets. Based on the tracked path, we then evaluate the target's size. To test our approach, we performed extensive simulations as well as eight sea experiments in different environmental settings to track both a scuba diver and a sandbar shark (Carcharhinus plumbeus). The simulation results show a tracking performance that is close to the Cramér-Rao lower bound, and the experiment results show a good tradeoff between detection rate and false alarm rate for a low signal-to-clutter ratio of 5 dB, and average tracking error of 1.5 and 6.5 m in the detections of a scuba diver and sandbar shark, respectively. For reproducibility, we share our sea experiment data.
|Number of pages||16|
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|State||Published - Mar 2019|
Bibliographical noteFunding Information:
This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant agreement 773753 (Symbiosis), and in part by the NATO Science for Peace and Security Programme under Grant G5293.
Manuscript received June 28, 2018; revised December 2, 2018 and January 31, 2018; accepted February 4, 2019. Date of publication February 13, 2019; date of current version April 11, 2019. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 773753 (Symbiosis), and in part by the NATO Science for Peace and Security Programme under Grant G5293. This paper was presented in part at the 176th meeting of the Acoustical Society of America, Victoria, BC, Canada, November 2018. The guest editor coordinating the review of this manuscript and approving it for publication was Prof. Martin Haardt. (Corresponding author: Roee Diamant.) R. Diamant, D. Kipnis, and A. Pinchasi are with the Department of Marine Technologies, University of Haifa, Haifa 3498838, Israel (e-mail:, firstname.lastname@example.org; email@example.com; apinchsi@ univ.haifa.ac.il).
© 2007-2012 IEEE.
- Underwater acoustic detection
- detection in a reverberant environment
- scuba diver detection
- shark detection
- tracking moving targets
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering