Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be investigated and understood. In this study, the Kirchhoff-ray mode (KRM) model was adopted to evaluate and analyze the acoustic TS of T. jarbua and its variations with the sound wave frequency, pitch angle distributions as well as morphological characteristics in the South China Sea. A total of 19 samples were captured and evaluated at four types of frequencies of 38 kHz, 70 kHz, 120 kHz, and 200 kHz. The results demonstrated that the TS of T. jarbua varied with the pitch angle shifts, and the number of secondary TS peaks increased as the increasing frequency accordingly. Two classic pitch angle distributions that included N[−5°, 15°] and N[0°, 10°] were adopted to calculate the average TS of T. jarbua. The fitted TS-L regression formulations and the standard b₂₀ form equations were determined at different pitch angle distributions as well as frequencies. These results could support the accurate and reliable UWA abundance estimation in the South China Sea to facilitate a better understanding of the abundance and population dynamics of T. jarbua.

With its superiorities of low cost, high flexibility and deployment convenience, small-size autonomous underwater vehicles (AUVs) have been extensively applied to perform a variety of undersea missions. While underwater acoustic (UWA) communication provides a practical way to establish a wireless link, it still poses a significant challenge due to the strict limitations of a small-size AUV platform in terms of load capacity, energy supply and cost. Orthogonal frequency division multiplexing (OFDM) has drawn extensive attention due to its high data rate capability and relative robustness to multipath, the performance of which is unfortunately sensitive to the widespread Doppler effect. While efficient Doppler compensation is significantly crucial for UWA OFDM mobile communication, most of the conventional approaches are conducted using software resampling, thus rendering a huge burden on memory and calculation capability as well as a considerable processing delay. In this paper, from the perspective of hardware completion an UWA OFDM communication payload based on STM32F407 processor is designed and implemented to facilitate agile Doppler compensation with low computational overhead. In particular, after estimating Doppler by calculating the time compression or extension of the preamble signal, Doppler compensation is performed by directly adjusting the direct digital frequency synthesizer (DDS)-driven sampling rate of the analog-to-digital converter (ADC). As the Doppler is compensated parallel to the ADC acquisition, processing delay and memory requirement can be avoided. Finally, hardware-in-loop (HIP) simulation is performed to demonstrate the effectiveness and superiority of the proposed system. The results show that the designed system has the potential to achieve an effective communication rate of 3.19 kbps with the admissible implementation overhead. Future work will entail the integration and performance evaluation of the proposed UWA OFDM communication payload on a practical small-size AUV platform.

Underwater sensor networks (USNs) are essential for marine monitoring, yet their performance is constrained by the instability of underwater acoustic (UWA) communication links. Orthogonal frequency division multiplexing (OFDM) is widely adopted in UWA systems for its high data rate capability and support for multi-user access, however, it remains highly sensitive to Doppler effects, which degrades demodulation performance.Hence, Doppler estimation and compensation are essential in UWA OFDM communications. The Doppler scale is typically estimated by the calculation of the optimal cross-ambiguity function (CAF) between the locally generated and received signals. However, this type of algorithm generates prohibitive computational complexity due to the necessity of exhaustive search over a two-dimensional delay and Doppler factor space. In this paper, a cross-ambiguity function hierarchical optimization (CAF-HO) framework is proposed to partition the Doppler estimation into two stages for low complexity Doppler estimation. Furthermore, the particle swarm optimization (PSO) is integrated with adaptive parameter control to effectively navigate the Doppler estimation landscape. The numerical simulation and sea trial experiment demonstrate the effectiveness and superiority of the proposed strategy over the CAF estimation and block estimation approaches. By diminishing computational burden and energy consumption, this framework facilitates the large-scale deployment of USNs crucial for marine monitoring, climate research, and maritime security.

Underwater sensor networks (USNs) constitute the fundamental infrastructure for marine monitoring and oceanographic data collection. The reliable transmission links are essential for the operation of these sensor nodes. Acoustic transmission provides an effective solution to meet this demand in the complex underwater environment. In particular, orthogonal frequency division multiplexing (OFDM) has attracted considerableattention in USNs due to its high data rate transmission and multi-user and multi-access capability. However, Doppler-induced time-scaling distortions in the practical marine environment severely degrade the performance of underwater acoustic (UWA) OFDM systems. Conventional cross-ambiguity function (CAF) methods generally necessitate long training sequences to ensure estimation accuracy, resulting in excessive signal-frame overhead. To address this issue, we propose a novel Bayesian optimization-based bit-correlation (BOBC) Doppler estimation algorithm that exploits the M-sequences to perform the bit-correlation with only two OFDM symbols. To avoid the computationally expensive fine-grid search typically used for the bit-correlation peak localization, we formulate the Doppler estimation as a black-box optimization problem and solve it via Bayesian optimization, which efficiently searches for the Doppler candidate associated with a high bit-correlation value without exhaustive fine-grid searches. Numerical simulations demonstrate that, under the same two-OFDM-symbol observation interval, the proposed BOBC algorithm reduces the root mean square error (RMSE) from approximately 1.2–1.5 Hz for the CAF and block estimation methods to approximately 0.35 Hz at a 10 Hz Doppler shift. When the CAF observation interval is extended to six OFDM symbols, its estimation accuracy becomes comparable to that of BOBC. In addition, the proposed BO strategy reduces the number of objective-function evaluations from 2001 to approximately 75 compared with the exhaustive bit-correlation search. This framework alleviates signal-frame overhead and computational burden and may offer potential gains for resource-constrained USNs.

Underwater sensor networks (USNs) are essential for marine exploration and monitoring, yet their performance is limited by transmission reliability in complex underwater environments. Underwater acoustic (UWA) transmission offers a practical solution, with orthogonal frequency division multiplexing (OFDM) extensively adopted for its high data rate and multiple access capability. However, OFDM is extremely vulnerable to Doppler-induced distortions, which degrade demodulation performance. Conventional cross-ambiguity function (CAF) methods estimate Doppler through signal-level correlation, but achieving high accuracy requires long training sequences, thus incurring frame overhead and reducing effective data rates. To address this issue, a novel bit-level correlation (BLC) Doppler estimation algorithm enables accurate estimation with diminished training overhead. A tailored OFDM frame uses the first two OFDM symbols, modulated with M-sequences, as training sequences. Demodulated bits are correlated with a local M-sequence via vector inner-product computation, converting correlation from the signal level to the bit level. Numerical simulations under three Doppler scales demonstrate the effectiveness of the BLC algorithm. By correlating the demodulated training bits with a local M-sequence reference, the proposed algorithm provides a favorable trade-off between training overhead and estimation accuracy under the tested simulation conditions.