The range resolution of high-resolution wideband radar is much smaller than the target size. Its echo signals tend to be diverse and sensitive to small changes of targets. Therefore, it is difficult to capture and distinguish the features in radar signals. In this article, we propose a radar target recognition pipeline based on a deep nested neural network. The framework consists of three parts: The translation sensitivity of the training data is first addressed in the preprocessing section. The second step is to obtain an embedded representation of the radar echo signals by the combination of the adjustment layer, convolutional neural network (CNN), and the squeeze and excitation (SE) block. Finally, the target is recognized through inputting embedded representation as a time sequence into the stacked bidirectional recurrent neural network (bi-RNN) based on an attention mechanism. Compared with the traditional methods, the proposed deep nested neural network extracts and takes advantage of the features of radar echo signals more effectively, including the envelope features and local physical structural features. The experimental results based on the test data indicate that the proposed method has a great advantage over other methods in the case of large data sets as well as small training data sets and is robust to the small translation of test samples and noises, exhibiting high engineering practical value.

Autonomous Underwater Vehicle (AUV) system has played an important role in complex Internet of Underwater Things (IoUT). As we know, single AUV is inefficient in collecting data in large-scale IoUT. In order to improve the timeliness of data, this paper uses multiple AUVs with specific formation shape to collect sensory data. However, the intermittent and unreliable characteristics of underwater acoustic communication between AUVs seriously affects the performance of formation control. To solve this issue, this paper mainly investigates formation control problem of leader-follower structured AUVs with state prediction estimation under the condition of unreliable underwater acoustic channel. Firstly, a novel formation control law derived from backstepping sliding mode method is proposed, and then a suitable Lyapunov functional is constructed to prove sufficient stability conditions. Secondly, Gaussian prediction model considering time delay and packet dropout has been designed to estimate the intermittent communication channel. Moreover, a new metric named formation uniform degree (FUD) is proposed to measure the degree of queue uniformity in a quantitative manner. Finally, extensive simulation results compared with traditional methods based on ideal channel model demonstrate the effectiveness of proposed formation control method.

Underwater vision-based detection plays an increasingly important role in underwater security, ocean exploration and other fields. Due to the absorption and scattering effects of water on light, as well as the movement of the carrier, underwater images generally have problems such as noise pollution, color cast and motion blur, which seriously affect the performance of underwater vision-based detection. To address these problems, this study proposes an end-to-end marine organism detection framework that can jointly optimize the image enhancement and object detection. The framework uses a two-stage detection network with dynamic intersection over union (IoU) threshold as the backbone and adds an underwater image enhancement module (UIEM) composed of denoising, color correction and deblurring sub-modules to greatly improve the framework’s ability to deal with severely degraded underwater images. Meanwhile, a self-built dataset is introduced to pre-train the UIEM, so that the training of the entire framework can be performed end-to-end. The experimental results show that compared with the existing end-to-end models applied to marine organism detection, the detection precision of the proposed framework can improve by at least 6%, and the detection speed has not been significantly reduced, so that it can complete the high-precision real-time detection of marine organisms.

The Autonomous Underwater Vehicle’s body attitude has a great influence on some specific underwater tasks, such as topographic prospecting, target detection, etc. Therefore, this paper investigates an improved integral sliding mode control (IISMC)-based attitude controller for AUV with model uncertainties and external disturbances to improve the ability of attitude tracking for AUV. To reduce the influence of strong interference on the integral term, the Gaussian function is introduced in integral sliding mode controller. Moreover, the Lyapunov function is used to prove the stability of IISMC-based attitude control law. Finally, the numerical simulations on MATLAB/Simulink are provided to demonstrate the proposed IISMC has smaller tracking error and converges faster than Sliding Mode Control (SMC) and Integral Sliding Mode Control (ISMC)-based attitude-control laws under different disturbances. Better yet, the effectiveness of the proposed IISMC-based attitude control law is tested in field experiments.

To improve the catastrophic forgetting that existed in the high-resolution range profile (HRRP) target recognition model of offline training, this paper proposed a continuous learning method of radar HRRP based on conditional variational auto-encoding and generative adversarial network (CVAE-GAN). Firstly, this method generates data through CVAE to simulate the real training data, and replays it in the series of subsequent tasks. By using generators, the proposed method does not need to save the original HRRP data and can ensure the privacy of the training dataset. Secondly, the proposed method takes the categorical data label as a generating condition, solves the unbalanced categories of the generated data samples, and improves the accuracy of the radar HRRP target recognition. Finally, by combining the attention mechanism and GAN network of the transformer, the generative capacity of the CVAE generator is enhanced effectively. In this paper, the continuous learning method is verified by a validation framework with three task settings. The experimental results show that the proposed method has a better continuous learning ability and better engineering practicality in various tasks compared with the previous regularization methods which can guarantee the privacy of the training dataset.

This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.