In shallow water, modal wavenumbers are useful for many applications. This paper presents an approach for wavenumber estimation of range-independent waveguide based on pressure and its finite difference (FD) data matrices built on the vertical linear array (VLA) data. The core of this method is that wavenumber information is expressed in the singular value decomposition (SVD) of the FD data matrix. Therefore, wavenumbers can be estimated directly by combining the SVDs of the pressure data matrix and FD data matrix. The method is validated on simulated data. The dependence of the proposed method on relevant parameters is discussed.

In the field of aerial photography, complete coverage path planning (CCPP) is the key to improving the task efficiency of unmanned aerial vehicles (UAVs) with a limited battery capacity. Previous CCPP algorithms consisted of Cellular Decomposition (CD) class, Probabilistic Roadmap (PRM) class, and Bio-inspired Neural Network (BINN) class. However, when faced with the complex environment of multiple forbidden areas, CD and PRM have high computational complexity and a long path length, and BINN has a large number of turns. In this paper, an improved back-and-forth (IBF) algorithm is proposed to achieve good performance in these three aspects at the same time based on a newly straight-turn mode and the corresponding auxiliary approaches (i.e. Local Traversal, Global Traversal, and continuity constraint function). An important difference between straight-turn mode and back-and-forth mode is that the next straight direction is not restricted to being parallel to the last straight direction but is determined by Local Traversal, which is introduced to evaluate the benefits of different paths and find the best direction for the next straight-turn mode at the ending cell. Global Traversal is available to choose a “jump” cell when "Dead zone" are faced. The continuity constraint function is used to maintain the continuity of the unexplored area. Under the comprehensive consideration of path length, computation time, and number of turns, the simulation and experimental results indicate that the proposed IBF algorithm has better performance in multiple forbidden areas.

Signal sparsity plays an essential role in signal compression and reconstruction. Prony-like methods are widely used in the relevant applications e.g., the recovery of signals with finite rate of innovation (FRI). However, these methods are usually limited to the structured function expansions of a single family, i.e., the considered structured functions belonging to the eigenfunctions of a single operator. In this paper, we investigate hybrid Prony models (HPMs), wherein functions can be expanded sparsely by the combination of eigenfunctions of two different operators L ₁ and L ₂ . Although the Prony-like methods cannot directly deal with HPMs, we show that HPMs can be directly converted to the generalized Prony model recently considered by Peter and Plonka when the operator pair { L ₁ , L ₂ } satisfies certain conditions; we refer to these functions as separable HPMs (SHPMs). Similar to the Prony method, the problem of nonlinear parameter estimation in SHPMs can be converted to the problem of finding the roots of polynomials. Examples and numerical results are given to illustrate SHPMs.

A multi-resolution estimation method for interference spectrum separation from a one-dimensional broadband acoustic intensity is presented in this letter. The key of the proposed method is the recursion reconstruction and singular value decomposition of a 2×L Hankel matrix, which is constructed by a broadband intensity data sequence of length L + 1. Numerical simulation results show that this method can be used to achieve the zero-phase-shift estimation of each interference spectrum without any prior environmental information.

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound source depth estimation algorithm, seabed independent depth estimation (SIDE) algorithm, is proposed. This algorithm combines the estimated mode depth functions, modal amplitudes, and the sign of each modal to estimate the sound source depth. The performance of the SIDE algorithm is analyzed by simulations. Results show that the SIDE is insensitive to the initial range of the sound source, the source depth, the hydrophone depth, the source velocity, and the type of the seabed. Finally, the effectiveness of the SIDE algorithm is verified by the SWellEX-96 data.

Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90.