We propose a backscattering compensation based procedure to enhance the performance of multibeam echosounder systems (MBESs). High resolution in a wide field of view is the main benefit of MBESs, and this property makes them increasingly important for measuring scattering in water body. The performance of these systems is vulnerable to the anisotropic characteristics of scatterers, and thus, the results provided by MBESs can be inaccurate. To enhance the performance of MBESs, we compensate for the echo data on the basis of backscattering strength. The compensation considers the reflected sound energy by introducing interlayer coefficients calculated from the amounts of energy that penetrate into and are reflected from each layer. To demonstrate the effectiveness of the proposed method, we perform numerical simulations with randomly positioned scatters having anisotropic properties. Furthermore, anechoic pool experiments are conducted using bubbles generated by air compressors and propellers. The after-compensation results reveal richer details, thereby confirming the effectiveness of the proposed method.

This paper proposes a method for visualizing three-dimensional non-uniformly sampled data from multibeam echosounder systems (MBESs), aimed at addressing the requirements of monitoring complex and dynamic underwater flow fields. To tackle the challenges associated with spatially non-uniform sampling, the proposed method employs linear interpolation along the radial direction and arc length weighted interpolation in the beam direction. This approach ensures consistent resolution of three-dimensional data across the same dimension. Additionally, an opacity transfer function is generated to enhance the visualization performance of the ray casting algorithm. This function leverages data values and gradient information, including the first and second directional derivatives, to suppress the rendering of background and non-interest regions while emphasizing target areas and boundary features. The simulation and experimental results demonstrate that, compared to conventional two-dimensional beam images and three-dimensional images, the proposed algorithm provides a more intuitive and accurate representation of three-dimensional data, offering significant support for the observation and analysis of spatial flow field characteristics.

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple frames. Specifically, an energy function is designed to jointly encode pulse similarity, motion continuity, trajectory persistence, data fidelity, and regularization, thereby reformulating multipath delay estimation as a global optimization problem. In order to balance the discreteness of observations and the continuity of trajectories, the optimization process is implemented alternating between discrete association (solved via $\alpha$-expansion) and continuous trajectory fitting (using weighted cubic splines). Furthermore, a dynamic hypothesis space expansion strategy based on trajectory merging and splitting is introduced to improve robustness while accelerating convergence. By exploiting both the intrinsic characteristics of correlation pulses in multi-frame processing and the physical properties of motion trajectories, the proposed method achieves higher tracking accuracy without requiring prior knowledge of the number of delay trajectories in a noisy environment. Numerical simulations under various noise conditions and sea trial results validate the superiorities of the proposed multipath delay estimation method.