Impulsive noise poses significant challenges to underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems, and existing estimation techniques often overlook the error propagation between carrier frequency offset (CFO) and impulsive noise. Motivated by signal-noise separation methodologies, this paper proposes a guard subcarriers based impulsive noise estimation method to estimate impulsive noise and CFO, mitigating error propagation. Unlike conventional systems, which place the null subcarriers uniformly, this paper categorizes null subcarriers into guard subcarriers and CFO-free subcarriers. Impulsive noise can be estimated based on CFO-free subcarriers without CFO contamination. Subsequently, CFO estimation is performed following impulsive noise cancellation, effectively eliminating the error propagation between impulsive noise and CFO. To enhance the robustness of impulsive noise estimation under harsh underwater environments, an adaptive sparsity detection algorithm is proposed, which minimizes the residual energy of CFO-free subcarriers. Additionally, a joint guard and pilot subcarriers based CFO estimation algorithm is designed to enhance the accuracy of CFO estimation. The efficacy of the proposed method is comprehensively validated through simulations and sea trial experiments, demonstrating superior performance compared to existing algorithms.

The technique of underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication has drawn significant attention from diverse marine fields due to its high-data-rate and multipath mitigation capability. However, constraints of adverse UWA channels, such as low signal-noise-ratio and time variations, generally require more pilot subcarriers to cope with, which unavoidably reduces the effective communication data rate. Thus, seeking a tradeoff between OFDM communication performance and pilot overhead poses significant challenges. To address this issue, a belief discrimination data reuse method is proposed to increase the pilot subcarriers by reusing the high belief data subcarriers as the effective pilot subcarriers to enhance the OFDM communication without actually increasing pilot overhead. Specifically, to ensure accuracy, our proposed method employs a hierarchical belief discrimination combining bit-level posterior probabilities from Tanner graph decoding and symbol-level Euclidean distance metrics. These reused high-belief subcarriers are subsequently integrated into the original pilot subcarriers for the next iteration. In addition, to mitigate the computational complexity associated with the iterative process, multiattribute termination conditions are established, which incorporate hard decision methodologies and first-order derivatives of extrinsic information. Numerical simulations and sea trial experimental results demonstrated the superiority of the proposed methods over existing alternatives across various code rates and pilot subcarrier configurations, facilitating the acquisition of underwater remote sensing information.

For deep-sea underwater acoustic (UWA) communication, large multipath time delay spread, that caused by spatial divergence effects of acoustic propagation along the deep-sea distance-depth dimension, exert extremely adverse influence on communication performance. Inspired by the fact that the large delay spread deep-sea channels tend to be clustered, and each cluster of which varies independently with respect to receiving element as well as time delay, in this article a multipath clusters (MCs) joint equalization approach is proposed to explore the spatial–temporal diversity of large delay spread deep-sea UWA channels. First we design a compact m-sequence synchronization sequence to extract MCs along different receiving element and time delay while achieving frame synchronization. Then, a sparsity-aware proportionate-type adaptive iterative algorithm is formulated under the recursive least squares (RLS) framework, by which, equalization output of each spatial–temporal-wise MC is aligned and combined to achieve joint equalization gain. Simulation results demonstrate that the proposed approach is capable of yielding superior performance in terms of output signal-to-noise ratio (SNR) and bit error rate (BER) compared to the traditional equalization approaches under large delay spread channels. Finally, in deep-sea trial error-free UWA communication with a maximum data rate of 6000 bps at a distance of 20 km is achieved by a two-element receiver, further demonstrating the effectiveness of the proposed algorithm.

The challenging problem of single-hydrophone underwater acoustic direction of arrival (DOA) estimation draws significant attention from diverse underwater drone applications due to its small size requirement and low hardware overhead, addressing which from the perspective of acoustic metamaterial retains a frontier. In this paper, an acoustic metamaterial shell with multiple pore-cavity structure is randomly designed to generate anisotropic direction-dependent frequency modulation (DDFM) effect, which enables single-hydrophone collaborative DOA estimation by exploring the sparsity of spatial target distribution and the pre-known DDFM pattern. To mitigate the deterioration of DDFM effect caused by multipath underwater acoustic channel, a metamaterial-assisted multipath decoupling (MAMD) sparse recovery method is further developed. Specifically, contribution of multipath channel response is decomposed into time delay and amplitude components, which are then recombined with pre-know DDFM pattern matrix and sparse direction vector, respectively, to achieve DOA estimation via sparse recovery algorithm. Numerical simulations and lake experiments onboard autonomous underwater vehicle (AUV) demonstrate that the proposed method reduces the root mean square error (RMSE) by approximately 7.8296° and improves the estimated success rate (SR) by about 7.4% compared to traditional array-based techniques.

Due to its high spectral efficiency and robustness in multipath channels, orthogonal frequency division multiplexing (OFDM) has been considered one of the most promising coherent underwater acoustic (UWA) communication technologies. Channel estimation (CE) plays an important role in OFDM receivers for mitigating the negative impact of UWA channels, unfortunately, the performance of conventional CE algorithms suffers from significant degradation under time-varying UWA channels due to the unavoidable mismatch between CE and OFDM demodulation. As channel estimators are generally designed to optimize signal-level cost functions, however, the results of which are used for bit-level OFDM demodulation. In this article, a minimum bit-error-ratio (BER) sparsity exploitation (MBSE) CE algorithm is proposed under the least squares (LS) framework to address this mismatch. By tuning the sparsity exploitation parameters, i.e., threshold and diffuseness, in a novel manner, the proposed algorithm is designed to adapt to the time-varying UWA channels to improve OFDM demodulation. Specifically, while the sparsity exploitation threshold is obtained via minimum BER searching to determine the dominant multipath arrivals, the structural sparsity parameter, defined as multipath diffuseness, is symbol-wise updated to track channel variations between adjacent symbols. Numerical simulation and sea trial experiments verify the performance enhancement of the proposed algorithm in terms of output signal-to-noise ratio, channel estimation mean square error, and BER under artificial and physical time-varying UWA channels, respectively.

Underwater acoustic (UWA) communication provides an effective way for underwater devices to establish wireless links, which is crucial for the development of Internet of Underwater Things (IoUT). Because noncoherent orthogonal frequency division multiplexing with M-ary frequency shift keying (OFDM-MFSK) adopts energy detection methods without channel estimation and channel equalization, it is not sensitive to the challenges posed by UWA channels, including low signal-to-noise ratio (SNR), large Doppler effect, and time-varying multipath propagation. As a result, it exhibits strong robustness for UWA communication. However, its spectral efficiency is severely limited. In this article, inspired by index modulation (IM) technology, OFDM-MFSK with IM systems are proposed. Specifically, two schemes are proposed: separate spectrum band OFDM-MFSK with IM (SOMI) and time spectrum band OFDM-MFSK with IM (TOMI). SOMI aims to overcome frequency-selective fading by leveraging the performance gain of multiple subbands, while TOMI is suitable for the channels with extremely limited bandwidth at the expense of time slots. To further enhance performance, a soft-decision algorithm, which is easily combined with channel decoding, is adopted for the proposed systems. The combination probability matrices are utilized to calculate the log-likelihood ratio for each bit. Finally, the efficiency of the proposed systems is demonstrated through simulation and sea trial experiments. In particular, by using a single receiving array, the proposed systems achieve error-free communication with a data rate of 2688 bps under an average SNR of 11.88 dB.

One effective means to enhance the quality of underwater acoustic (UWA) communication is to design a reasonable receiver. In this study, we propose a novel fast Kalman (FK) adaptive equalizer developed to meet the requirements for noise robustness and Doppler shift resistance in single-carrier UWA communication. We integrate phase tracking performance into the FK adaptive equalizer, enabling rapid compensation for rotating taps in the face of complex time-varying channels, further enhancing equalization performance. At-sea experiments demonstrate that the FK adaptive equalizer effectively improves the bit error rate of single-carrier UWA communication. In addition, the purpose of the proposed FK algorithm is to represent the gain vector using weight vectors derived from the forward and backward prediction problems, avoiding the inverse operation of coefficient matrices, resulting in linear$\mathcal {O}(L)$complexity. Simulation results demonstrate that the FK algorithm strikes an effective balance between steady-state mean squared deviation and computational efficiency. To validate the equalization performance of FK adaptive equalizer, we propose the single-carrier communication framework for simulation and at-sea experiments, detailing both the transmission frame parameters and test parameters. Compared to other equalizers, FK adaptive equalizer effectively achieves reliability and efficiency in underwater data transmission.

Sound source localization provides an absorbing capability for unmanned aerial vehicles (UAVs) in scenarios, such as search and rescue operations. The shape fusion between the sound array and UAVs forms a special conformal property that is drawing more and more attention. However, the inevitable shadow effect caused by shape fusion seriously degrades the degrees of freedom (DOF) of the array. In this article, a signal reconstruction-based direction of arrival (DOA) estimation method is proposed to address this limitation. First, we establish a restricted signal model for the conformal cylindrical array (CCA), and then based on frequency domain energy detection, the elements are divided into receiving restricted elements and receiving normal elements. Second, according to the position vector of receiving restricted elements, the approximate range of the DOA is roughly estimated to reduce the complexity. Meanwhile, the signals of receiving restricted elements are reconstructed on the basis of receiving normal elements to eliminate the shadow effect and increase the DOF. Finally, in the estimated approximate range of the DOA, the precise DOA is estimated by peak search. We also derive the 2-D Cramer–Rao lower bound (CRLB) for the CCA. Simulations show that the proposed SR-MUSIC-RS method can achieve satisfactory performance with lower complexity, and the root mean squared error is close to that of general signal model with normal elements.

Channel estimation plays a crucial role in Internet of Underwater Things networks. Traditional channel estimation algorithms have limited performance improvement under the block-sparsity underwater acoustic channel, and the distributed compressed sensing (DCS) method has not been researched under block-sparsity recovery. In this article, we propose DCS with block sparsity for underwater acoustic channel estimation under short observation length and low SNR scenarios. Specifically, the underwater acoustic channels are partitioned some sub-blocks that have the identical size, and the simultaneous block orthogonal matching pursuit algorithm (SBOMP) is proposed to enhance the channel estimates that have common delays. However, the number of nonzero taps located in a partitioned sub-block does not equal to the size of partitioned sub-blocks, and the channel between adjacent two data blocks may have slightly difference, when the SBOMP algorithm is applied, there would be much estimated noise. In order to address this problem, in this article, we also propose dynamic block sparsity-based SBOMP which is referred to as DSBOMP. The proposed DSBOMP consists of the SBOMP algorithm, first-order derivative of the residual method, and parallel comparison strategy. The first-order derivative is used to remove some negligible taps and dynamically measure the number of significant taps; the parallel comparison strategy is used to check whether the current taps are available. Both simulation and sea trial data indicate that, our proposed SBOMP and DSBOMP algorithms have better channel estimation performance than tradition algorithms under short observation length and low SNR. Moreover, our proposed DSBOMP achieves the best communication performance.

Orthogonal signal division multiplexing (OSDM) is an emerging method for Internet of Underwater Things (IoUT) networks. The impulsive noise, the carrier frequency offset (CFO), and the time-varying underwater acoustic channel decrease the performance of underwater acoustic OSDM communications. In this article, the orthogonal projection and distributed compressed sensing (DCS) methods are utilized to facilitate the CFO, impulsive noise, and channel estimation. First, the received signal is compensated by different tentative CFOS. Then an orthogonal projection matrix is constructed and projects the received signals into a specific subspace, where the channel portion is zero. Second, the received signals after projection are combined to improve impulsive estimation under the framework of DCS method. Finally, an first-order derivative of residual method is introduced to measure the amount of impulsive noise dynamically. The proposed methods adopt pilot vectors for estimating the CFO, the impulsive noise, and the channel, as a result, the bandwidth is improved significantly. Moreover, the proposed methods improve the impulsive noise estimation under and lower SNR and fewer number of pilot vectors. Both numerical simulation and sea trial data are used to evaluate the performance of our proposed methods, the experimental results demonstrate the effectiveness of our proposed methods.

With the massive development of underwater small robotic vehicles and matching acoustic modems, applications for the Internet of Underwater Things (IoUT) are emerging. IoUT involves communication between the nonsynchronized network nodes organized in a mesh. A limiting factor of such communication is the so-called near-far effect, where transmissions from a node (near) close to a common receiver block the transmissions of a farther node (far). Due to the high-power attenuation in the underwater acoustic channel, near-far effect is common in underwater acoustic communication networks, and the phenomena occurs even for a distance ratio of 80% between the near and far nodes to the receiver, and the large number of nodes in the IoUT compounds the effect of this phenomena. While the current approaches only consider the jamming effect to the far signal, in this article, we consider canceling the interference from both sources by estimating and equalizing the channels on parallel, thereby significantly improving the decoding of both signals. As a result, IoUT performance improves. To limit mutual interference, we propose an automatic switching mechanism that controls the cancelation operation both in channel estimation and channel equalization. The simulation results show that our approach obtains significant improvement in communication from both near and far nodes. Results from a designated sea trial demonstrate that when both nodes are affected by their mutual transmissions, our proposed method improves the output signal-to-noise ratio (SNR) significantly.

Micro-sized autonomous underwater vehicles (μAUVs) are well suited to various applications in confined underwater spaces. Acoustic communication is required for many application scenarios of μAUVs to enable data transmission without surfacing. This paper presents the integration of a compact acoustic communication device with a μAUV prototype. Packet reception rate (PRR) and bit error rate (BER) of the acoustic communication link are evaluated in a confined pool environment through experiments while the μAUV is either stationary or moving. We pinpoint several major factors that impact the communication performance. Experimental results show that the multi-path effect significantly affects the synchronization signals of the communication device. The relative motion between the vehicle and the base station also degrades the communication performance. These results suggest future methods towards improvements.