Robust underwater direction-of-arrival tracking with uncertain environmental disturbances using a uniform circular hydrophone array.

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, considering that an unknown underwater environment results in uncertain disturbances to the measurements, robust underwater DOA tracking with regard to uncertain environmental disturbances was studied. Because the uniform circular array (UCA) is free from the port and starboard ambiguity problem, a UCA was used to obtain the measurements for a long-time tracking scenario. First, a kinematic model of an underwater target and a measurement model based on the received signal of the UCA were established. Then, a DOA tracking algorithm was derived based on the extended Kalman filter (EKF), whose performance is significantly affected by the accuracy of the measurement noise covariance matrix (MNCM). Finally, considering that uncertain disturbances carry out unstable measurement noise, the modified Sage-Husa algorithm was used to obtain accurate MNCMs during the process of the derived EKF-based DOA tracking algorithm. Thus, a robust DOA tracking method with uncertain environmental disturbances using a UCA was proposed. The accuracy and reliability of the suggested method was verified via Monte Carlo simulations of a DOA tracking scenario and an experiment in the South China Sea in July 2021.

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys.

Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer-Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target's dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances.

The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target's kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.

Robust underwater direction-of-arrival tracking based on variational Bayesian extended Kalman filter.

The variational Bayesian extended Kalman filter (VB-EKF) based robust direction-of-arrival (DOA) tracking technique is proposed to make reliable estimations of the bearing angle of an underwater target with uncertain environment noise. By utilizing the VB-EKF scheme, the uncertain measurement noise caused by an unknown underwater environment along with the bearing angle of the target can be estimated simultaneously to provide reliable results at every tracking step. The proposed technique is demonstrated and verified by the sea trial data from the South China Sea in July 2021 and both the robustness and accuracy are proved superior to the traditional DOA estimating methods.

A time-frequency analysis approach to dispersion curve estimation for single-hydrophone acoustic signals in shallow water.

This Letter provides an advanced time-frequency analysis method for quickly and precisely estimating dispersion curves of low-frequency impulsive sounds propagated in shallow water. First, the modal chirplet transform is derived based on an approximated variation law of the modal instantaneous frequency, which can omit the modal filtering step of warping-based methods. Then an extension modal chirplet transform (EMCT) is developed to further improve its potential for automation, together with a trick for fast computation. EMCT is applied to a recorded signal to demonstrate that the method can achieve high time-frequency concentration for modal components in a significantly decreased run time.