Wideband direction of arrival (DOA) estimation using a sensor array plays a fundamental role in passive sonar signal processing. Although sparsity-based DOA estimation methods can attain high resolution in the condition of few snapshots and low signal-to-noise ratio, the localization accuracy is seriously affected by strong interferences. In this paper, a matrix filter with nulling (MFN) is used to pass weak targets in sector-of-interest (passband) while attenuating the out-of-sector (stopband) interferences by forming deep nulls toward the directions of interferences adaptively. Then, a method based on sparse spectrum fitting (SpSF) and MFN is proposed to localize closely spaced wideband signals in a strong interference environment. In comparison with the minimum variance distortionless response and SpSF, the proposed method achieves higher localization accuracy, which is verified by simulation and experimental results.

Several time-domain wideband beamformers with the response variation (RV) constraint were recently proposed to constrain the consistency of the main lobe responses over the frequency band of interest. However, when the model mismatch is severe, these beamformers may not be robust to suppress wideband interferences effectively. Instead of adding more uncertainty constraints to enhance the robustness, we attempt to remove the desired signal component from the stacked sample covariance matrix by reconstructing a wideband stacked interference-plus-noise covariance matrix (SINCM). To overcome the model mismatch for an array without calibration, we introduce a possible mismatched stacked steering vector (SSV) set, and we reconstruct the SINCM by integrating all possible mismatched SSVs over the interference angular sector and all sub-bands. Then we correct the desired signal SSV using two different approaches, one is solving an optimization problem that maximizes the array output, another is the eigen-decomposition of the reconstructed stacked desired signal covariance matrix (SDSCM). By transforming the RV inequality constraint into a weighted part of objective function, the FIR coefficient vector of the proposed wideband beamformer can be calculated in a closed form as a function of the reconstructed SINCM and the estimated SSV of the signal-of-interest (SOI). Numerical simulations show robust performance in the case of unknown SSV mismatch. An acoustic experiment is carried out on a linear hydrophone array, and the result validates that the proposed beamformer provides better performance compared with other wideband adaptive beamformers.

The underwater direction-of-arrival (DOA) tracking using hydrophone array is an important research subject in passive sonar signal processing. To deal with the unknown measurement noise results from underwater environmental noise, the existing variational Bayesian adaptive extended Kalman filter (VB-AEKF) technique for robust underwater DOA tracking jointly estimates the measurement noise covariance matrix and the bearing angle of a target. The iteration process and the nonlinear measurement model result in significant computational complexity of the VB-AEKF. By deriving computational process which is equivalent to VB-AEKF but more computationally efficient, a fast variational Bayesian adaptive extended Kalman filter (FVB-AEKF) for robust DOA tracking is proposed. The proposed method is tested via Monte Carlo simulations of an underwater DOA tracking scenario and an experiment in the South China Sea in July 2021. The results of the simulations and the experiment verified the accuracy and the computational efficiency of the FVB-AEKF.

When the intersensor spacing of a uniform linear array (ULA) is larger than the half-wavelength of an incident narrowband signal, spatial aliasing is generated. For broadband signals, the broadband spatial spectrum is still affected as a result of the spatial aliasing in each frequency bin. In this paper, an aliasing-free broadband direction-of-arrival (DOA) estimation algorithm for ULAs is proposed. First, an array output is constructed with a given Gaussian random sequence from the direction $\vartheta$ . Then, a frequency-difference (FD) operation is conducted, which multiplies the array observation in the frequency bin $f$ by the conjugate form of the constructed array output in the frequency bin $f+\Delta f$ . Thus, an equivalent array output at a desired frequency $\Delta f$ is obtained, whose wavelength is equal to twice the intersensor spacing. In this manner, an aliasing-free spatial spectrum in the FD domain is achieved. Scanning the direction $\vartheta$ , the DOA of signals is finally estimated based on the difference between the peaks in the aliasing-free spatial spectrum and direction $\vartheta$ . The proposed method can achieve a satisfactory estimation even in a strong interference environment. The simulations and experimental results are included to demonstrate the superiority of the proposed method.

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.

White Gaussian noise assumption is widely used in the field of spatial spectral estimation. But, the practical ambient noise usually does not satisfy this assumption, which may seriously affect the performance of direction of arrival estimation. This letter presents a linear noise model that can provide a more complete description of the practical ambient noise field, and then proposes a directional noise field sparse spectrum fitting (DN-SpSF) algorithm to estimate the spatial spectral in a directional noise field. Simulations and experimental results demonstrate the good performance of the DN-SpSF algorithm in a spatially directional noise environment.