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.

Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments.

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