Adaptive modulation and coding (AMC) is an effective technique for mitigating the severe dynamics of underwater acoustic (UWA) channels. However, its performance strongly relies on accurate channel state prediction, and prediction errors can cause significant performance degradation. Most existing approaches primarily focus on time-domain (TD) prediction, but the rapid variability of UWA channels makes long-term prediction highly challenging and limits their effectiveness. To overcome this limitation, we exploit the relative stability and sparsity of channel parameters in the delay–Doppler (DD) domain and propose a multi-scale convolutional long short-term memory (ConvLSTM) prediction framework. The DD-domain formulation reduces model complexity and supports multi-step forecasting, while the multi-scale architecture captures channel dynamics across different temporal scales and structural levels. The effectiveness of our method is validated through extensive experiments on real-world UWA measurements. The proposed framework achieves a normalized mean square error (NMSE) of 0.0818 in the DD-domain, significantly lower than the 0.2196 NMSE of direct TD prediction. Compared to conventional predictors, the proposed framework consistently reduces NMSE by approximately 70% across multiple datasets, thereby enhancing the robustness and feasibility of long-horizon channel forecasting for AMC systems.

Affine Frequency Division Multiplexing (AFDM) is an advanced communication waveform designed specifically for time-varying channels. This chirp-based multicarrier modulation technique is computationally efficient, which enables compact sonar implementations while achieving robust sensing performance through flexible parameter adjustments. Such characteristics make AFDM well-suited for Integrated Systems for Underwater Detection and Communication (ISUDC). In this paper, we explore an AFDM-based ISUDC system and propose a waveform model capable of transmitting multiple symbols to increase data capacity. We derive the Wideband Ambiguity Function (WAF) for this waveform and enhance it using complementary sequence coding, which reduces sensitivity to WAF variations and improves detection accuracy. Simulation results demonstrate that the proposed AFDM-based ISUDC waveform, featuring both multi-symbol support and complementary sequence coding, increases data capacity and improves communication bit error rate (BER) compared to traditional ISUDC waveforms. Additionally, the optimized WAF achieves enhanced detection performance, fulfilling critical ISUDC requirements.

Orthogonal time frequency space (OTFS) modulation has garnered significant interest for its robust performance in fast time-varying channels, making it suitable for mobile underwater acoustic (UWA) communication system. This article introduces OTFS modulation to the UWA system and proposes a low-complexity minimum mean-squared error (MMSE) turbo equalization method. Leveraging the characteristics of UWA channels in the delay-Doppler (DD) domain, the method employs symbol-level MMSE equalization. By focusing processing on signals within the DD domain’s interference range, it reduces the channel matrix size, thereby lowering complexity. Given the long delay spread and large Doppler shift of UWA channels, symbol-level MMSE equalization inherently involves high complexity. To mitigate this, we propose two methods to further reduce the computational load associated with matrix inversion. First, we utilize common blocks in the channel matrix and employ a block iterative matrix inversion algorithm to retain computational results, thereby avoiding repeated inversions of the large dimensional matrix. Additionally, we enhance the diagonal dominance property of the channel matrix using the discrete Fourier transform (DFT) matrix. Subsequently, we approximate the inversion using the second-order Neumann series decomposition, further lowering computational complexity. Simulation results and experimental validations at Danjiangkou Lake demonstrate the efficacy of the proposed low-complexity iterative equalization algorithm.

Integrated systems for underwater detection and communication (ISUDC) plays a pivotal role in improving sonar integration and efficiency and has become a key research focus. This article tackles the underwater doubly dispersive wireless channel (DDWC) by introducing a novel transmitter side waveform design and a receiver side channel estimation algorithm based on affine frequency division multiplexing (AFDM) within the ISUDC framework. At the transmitter we employ AFDM as the core signal and target minimization of weighted sidelobes in the wideband ambiguity function (WAF). We use numerical analysis to quantify coding effects on the WAF and apply optimized random phase perturbations in P4 encoding via particle swarm optimization (PSO) to enhance detection and improve time Doppler resolution. At the receiver we develop a sparse channel estimation method based on an affine Fourier dictionary, which uses pilot signals to estimate phase perturbations and exploits delay-Doppler sparsity to improve accuracy in dynamic underwater environments while reducing multipath interference. We also derive new bounds on the pairwise error probability (PEP) for underwater acoustic DDWC, including numerical lower bounds and Chernoff upper bounds. Simulations demonstrate that jointly optimizing waveform design and channel estimation reduces PEP and normalized mean-square error (NMSE), provides superior detection for consecutive identical coded symbols and yields an ideal “thumbtack” shaped WAF. The proposed framework delivers a reliable and efficient solution for ISUDC in complex underwater environments.

Efficient target localization is critical to many marine applications in the Internet of Underwater Things (IoUT). Doppler effect becomes more predominant in the underwater environment when the relative speed of the moving object, i.e., the observer, to the signal propagation speed in water is much larger than that in the air. This along with the observer-target geometry will bring in a notable impact on the localization performance. In this article, we derive the Cramér–Rao lower bound (CRLB) and formulate the A-optimality criterion-based observer trajectory optimization problem to improve the localization performance based on time delay and Doppler shift measurements. We show that in the worst case scenario, there will be a conflict between the nonaccessible zone constraint and the observer dynamics constraint, which will lead to an erroneous result. To address this problem, we propose a warning zone-based augmented Lagrange multiplier method (ALMM) where the nonaccessible zone constraint is relaxed to resolve the conflict and ensure the nonaccessible requirement of the targeted zone is maintained. Performance evaluations are conducted through extensive simulations for different scenarios, and the results are compared to other methods with or without trajectory optimization. We demonstrate that trajectory optimization using time delays and Doppler shifts can greatly improve the target localization accuracy in underwater networks.

In this paper, we propose a novel clustered multitask diffusion RLS (MT-DRLS) algorithm over network to further improve the performance of its counterpart, the multitask diffusion LMS (MT-DLMS) algorithm. Its transient behavior is investigated, in the mean and mean-square error sense. Simulation results illustrate the significant improvement of the MT-DRLS over the MT-DLMS in terms of convergence rate and steady-state error, as well as the accuracy of the theoretical findings.

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We propose a Taylor expansion multiple signal classification (TE MUSIC) method for joint direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic multiple-input multiple-output (MIMO) array. First, using a Taylor expansion of the steering vector, a two-dimensional (2D) search in the conventional MUSIC method for MIMO arrays is reduced to a two-step one-dimensional (ID) search in the proposed TE MUSIC method. Second, DOAs of the targets can be achieved via Lagrange multiplier by a ID search. Finally, substituting the DOA estimates into the 2D MUSIC spectrum function, DODs of the targets are obtained by another ID search. Thus, the DOD and DOA estimates can be automatically paired. The performance of the proposed method is better than that of the MIMO ESPRIT method, and is similar to that of the 2D MUSIC method. Furthermore, due to the ID search, the TE MUSIC method avoids the high computational complexity of the 2D MUSIC method. Simulation results are presented to show the effectiveness of the proposed method.

In multi‐platform surveillance system, a prerequisite for successful fusion is the transformation of data from different platforms to a common coordinate system. However, some stochastic system biases arise during this transformation, and they seriously downgrade the global surveillance performance. Considering that the target state and the system biases are coupled and interactive, the authors present a new recursive joint estimation (RJE) algorithm for registering stochastic system biases and estimating target state. First, the relationship between system biases estimation and target state estimation is derived. Second, the RJE framework is introduced on the basis of the proposed relationship. Representing the different behavioural aspects of the motion of a maneuvering target is difficult to achieve with a single model in a multi‐platform target tracking system. By accounting for the non‐linear and/or non‐Gaussian property of the dynamic system, they modify the interacting multiple model–particle filter framework to estimate parameters. This approach considers not only the influence of the system biases, but also the covariance of state on the basis of multiple‐particle statistics. Simulation results reveal the superior performance of the proposed approach with respect to the traditional algorithm under the same conditions.