The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent simulations are conducted to determine the impacts of receiving depth, sea bottom boundary, and sea surface boundary on channel estimation. The simulations show that UWA channel estimation is greatly affected by underwater environments. The effect can be mainly attributed to changing acoustic rays tracing which result in fluctuating time delay and amplitude. With 10 m receiving depth and flat sea bottom, the channel estimation achieves optimal performance. Further study indicates that the sea surface has stochastic effects on channel estimation. As the significant wave height (SWH) increases, the average performance of channel estimation shows improvements.

In this paper, a generalized L-shaped nested array based on the fourth-order difference co-array is proposed for two-dimensional (2D) directions’ estimation. The new structure framework makes full use of the physical sensor locations to form a virtual uniform rectangular array (URA) as large as possible. As it utilizes the fourth-order difference instead of the traditional second-order difference result, this structure framework can acquire a much higher degree-of-freedom (DOF) than the existing 2D sparse arrays. The proposed structures have two advantages. One is that the subarrays can be chosen as any nested-class arrays, which makes the sparse array design more flexible. We can choose arbitrary subarray structures for DOF enhancement purposes. Another advantage is that the relative position of two subarrays can be set as any integral multiple of half wavelength. This means that two subarrays can be located as far as possible so that the relative influence between two physical subarrays can be ignored. The DOFs of several typical generalized L-shaped nested arrays (GLNAs) are compared in this paper. By setting the subarrays as different types and the relative position as a special value, a special GLNA is presented. Simulations show that GLNAs have obvious superiority in 2D direction-of-arrival estimation.

Recently, the concept of the difference and sum coarray has attracted increasing interest in the direction of the arrival estimation field because it can generate enhanced degrees of freedom. In this paper, we propose an improved transformed nested array design strategy by relaxing the constraints on the dense subarray of the transformed nested array. Then, three conditions are given for the array design to ensure the continuity of the difference and sum coarray. Based on the strategy, we develop a novel nested configuration named coprime transformed nested array (CTNA) whose dense subarray is a coprime structure, and the closed-form expressions for the sensor positions and the range of consecutive coarray are derived. CTNA can increase the number of degrees of freedom (DOFs) compared to the existing nested arrays, while the mutual coupling effect can be maintained at the same low level as the coprime arrays, which indicates that CTNA has the merits of both nested array and coprime array. Numerical simulations are performed to verify the superiority of the proposed array configuration in terms of the number of DOFs, mutual coupling and direction of arrival (DOA) estimation accuracy.