An efficient formation-keeping strategy is essential for unmanned surface vehicles (USVs) to achieve complex cooperation missions in the Marine Internet of Things (MIoT) system. However, traditional methods make generating an efficient strategy to adapt to different formation patterns difficult in dynamic MIoT. To address this, we enhance the Deep Deterministic Policy Gradient (DDPG) algorithm and propose a novel formation control strategy generation approach. First, we design a generic reward mechanism based on the virtual leaderfollower strategy to adapt to different formation patterns, simplify the design process, and optimize the formation control. Then, we adopt the intrinsic curiosity module (ICM) to alleviate the problem of sparse rewards and the prioritized experience replay (PER) mechanism to improve the utilization of experience and accelerate the learning rate. In addition, a Gaussian noise model is integrated into the DDPG approach to simulate various external disturbances, which can improve the robustness of the generated strategy. Finally, we built a virtual simulation environment based on Unity3D and conducted field tests to verify the feasibility and superiority of our approach.

In order to obtain the aerodynamic parameters of a high-spinning projectile, a new parameter identification method is proposed based on the Cubature Kalman Filter (CKF). First of all, the motion equation of the spinning stabilized projectile is established by the 4D trajectory model. Second, unknown parameters are added to the state vector to obtain the augmented state vector. Next, a new filter is designed for the identification of the unknown parameters based on the basic theory of the CKF. Finally, the simulation results of the CKF are compared with those of the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The results of comparison show that the CKF method can effectively identify the aerodynamic parameters and the identification error is less than 1.2%. The CKF method has greater accuracy than the EKF and the UKF.

The acceleration imaging algorithm of missile-borne SAR deals with acceleration and high squint angle using an equivalent range model. However, the original missile-borne SAR acceleration imaging algorithm will be ineffective because the phase error compensation for the center point of the scene is no longer applicable to all point targets in the presence of increased acceleration. To address this problem, this paper makes improvements to the missile-borne SAR acceleration imaging algorithm and adds compensation measures for slant distance approximation error. The corresponding imaging algorithms are designed and the space-variant errors of the range and azimuth are compensated respectively. Simulation experiments verify the accuracy and effectiveness of the proposed algorithm.

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency.

In order to improve mathematical model accuracy of electromechanical actuator (EMA) and solve the problems of low-frequency response and large overshoot for nonlinear systems by using traditional proportional integral derivative (PID) algorithm, a fuzzy single neuron (FSN)-PID algorithm is proposed. Firstly, a complete multi-nonlinear dynamic model of EMA is constructed, which introduces internal friction and current limiter of brushless direct current motors (BLDCMs), dead zone backlash of gear trains, and LuGre friction between output shaft and fin. Secondly, a FSN-PID controller is introduced into the automatic position regulator (APR) of EMA control system, where the gain coefficient K of SN algorithm is adjusted by fuzzy control, and the stability of the controller is proved. In addition, simulations are conducted on the response effect of different fin positions under different algorithms for the analysis of the 6° fin position response; it can be concluded that the rise time with FSN-PID algorithm can be reduced by about 4.561% compared to PID, about 1.954% compared to fuzzy (F)-PID, about 0.875% compared to single neuron (SN)-PID, and about 0.380% compared to back propagation (BP)-PID. For the 4°-2 Hz sine position tracking analysis, it can be concluded that the minimum phase error of FSN-PID algorithm is about 0.4705 ms, which is about 74.44% smaller than PID, about 73.43% smaller than F-PID, about 17.24% smaller than SN-PID, and about 10.81% smaller than BP-PID. Finally, wind tunnel experiments investigate the actual high dynamic flight environment and verify the excellent position tracking ability of FSN-PID algorithm.

The traditional hyperbolic range equation model will produce large errors in the spaceborne sliding spotlight synthetic aperture radar with large azimuth time. Although the equivalent acceleration range model (EARM) can deal with the problem of large azimuth scenes well, the interpolation steps from azimuth resampling make the computational burden of algorithm very huge. In response to this problem, this paper improves EARM by dividing the range model into two parts: residual terms of azimuth time and equivalent range expression. The frequency domain imaging algorithm designed for the new range model omits the interpolation step of azimuth resampling. The simulation experiments verify the efficiency and validity of the proposed methodology.