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 Cramer–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.

/pdf/an-active-acoustic-track-before-detect-approach-for-finding-216gbpq7it.pdf

An Active Acoustic Track-Before-Detect Approach for Finding Underwater Mobile Targets

Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due to energy constraints, in off-shore monitoring platforms algorithms should operate in real time with limited power consumption. In this paper, we present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient pre-processing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. The CDA is trained on a large database of more than 20,000 reflection patterns collected during 50 designated sea experiments. System performance is then evaluated on a controlled dataset, for which ground truth information is known, as well as on recordings collected during different sea experiments. Results show that, compared to the benchmark, our method achieves a favorable trade-off between detection and false alarm rate, as well as improved tracking accuracy.

https://www.mdpi.com/1424-8220/20/10/2945/pdf

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets.

Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2015

/pdf/track-before-detect-for-active-sonar-hzhgl503gk.pdf

Track-before-detect for active sonar.

The previous set of chapters introduced a collection of different extensions to the core H-PMHT that expand its range of applications and improve the quality of its output. These chapters have been rather theoretical and highly mathematical. We now shift the focus to applications and provide a series of examples of how the various versions of H-PMHT described in this book can be used in practice. This chapter presents applications of H-PMHT to surveillance problems using radar and sonar.

Radar and Sonar Track-Before-Detect

The towing stability of the towed body has an important impact on the data collection quality of the mounted sensors. In this paper, a novel deep-sea towed body control technology based on adjustable hydrofoils is proposed, and a hydrodynamic performance optimization and adjustment prediction strategy is established. Through computational fluid dynamics (CFD) simulation, the effectiveness of the adjustable hydrofoils in improving the towing stability of the towed body is verified, and the hydrodynamic mechanism of the adjustable hydrofoils is analyzed. The influence weight and influence law of the hydrofoil structural parameters on adjusted variables are determined by sensitivity analysis and main effect analysis, and the hydrodynamic performance optimization of the adjustable hydrofoil is completed based on the fast elitist Non-dominated Sorting Genetic Algorithm III (NSGA-III). A bidirectional Gaussian Process (GP) prediction model with respect to the angle of attack of hydrofoils and adjusted variables is established. The prediction model shows high prediction accuracy and strong generalizability, with the prediction error within 4%. The effectiveness of the novel towed body control technology is verified through sea trials. This paper provides reference and guidance for the development and engineering application of the towed body control technology. 

Hydrodynamic performance optimization and adjustment prediction of a novel deep-sea towed body with adjustable hydrofoils

Conventional active sonar processing systems typically reduce the sensor data from an intensity map to a pointmeasurement form via a detection thresholding process. This approach is often sufficient for detecting and tracking high signal-to-noise-ratio (SNR) targets but becomes more challenging for low SNR targets. Track-Before-Detect (TkBD) is an alternative tracking technique that supplies raw sensor data to the tracker to determine the presence and kinematic state of a target. This paper considers the application of TkBD to an active sonar tracking problem using the Histogram-Probabilistic Multi-Hypothesis Tracking (H-PMHT) algorithm. To aid in the detection of targets in the sensor image, the measurement model for the standard H-PMHT algorithm is extended to incorporate a bearingdependent point spread function. Using real data gathered from sonar trials, the performance of the resulting multitarget TkBD algorithm is shown to be more robust at low SNR levels when compared against a conventional point measurement tracker based on Integrated Probabilistic Data Association.

/pdf/track-before-detect-for-an-active-towed-array-sonar-c6yy9ua829.pdf

Track-Before-Detect for an Active Towed Array Sonar

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Track-Before-Detect for an Active Towed Array Sonar