In previous studies, the semi-strapdown inertial navigation system (SSINS), based on microelectromechanical system (MEMS) sensors, had realized cross-range measurement of attitude information of high-spinning projectiles through construction of a “spin reduction” platform of the roll axis. However, further improvement of its measurement accuracy has been difficult, due to the inertial sensor error. In order to enhance the navigational accuracy, a periodically rotating method is utilized to compensate for sensor error, which is called rotation modulation. At present, the rotation scheme, as one of the core technologies, has been studied by a lot of researchers. It is known that the modulation angular rate is the main factor affecting the effectiveness of error modulation. Different from the long-endurance and low-dynamic motion characteristics of ships, however, the short-endurance and high-dynamic characteristics of the high-spinning projectile not only require the modulation angular rate to be as fast as possible but, also, the influence of the rotation speed error caused by rotating mechanism errors cannot be ignored. Combined with the rotation speed error of the rotating mechanism, this paper explored the relationship between modulation angular rate, device error, and the navigation error, and then proposed a design method for optimal modulation angular rate. Experiments were carried out to validate the performance of the method. In addition, the proposed method is applicable for rotation modulation systems with different types of motors as the rotating mechanism.

Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

The performance of integrated Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) navigation often declines in complex urban environments due to frequent GNSS signal blockages. This poses a significant challenge for autonomous driving applications that require continuous and reliable positioning. To address this limitation, this paper presents a novel hybrid framework that combines a deep learning architecture with an adaptive Kalman Filter. At the core of this framework is a Temporal Convolutional Network and Bidirectional Long Short-Term Memory (TCN-BiLSTM) model, which generates accurate pseudo-GNSS measurements from raw INS data during GNSS outages. These measurements are then fused with the INS data stream using an Adaptive Noise-Regulated Iterated Extended Kalman Filter (ANR-IEKF), which enhances robustness by dynamically estimating and adjusting the process and observation noise statistics in real time. The proposed ANR-IEKF + TCN-BiLSTM framework was validated using a real-world vehicle dataset that encompasses both straight-line and turning scenarios. The results demonstrate its superior performance in positioning accuracy and robustness compared to several baseline models, thereby confirming its effectiveness as a reliable solution for maintaining high-precision navigation in GNSS-denied environments. Validated in 70 s GNSS outage environments, our approach enhances positioning accuracy by over 50% against strong deep learning baselines with errors reduced to roughly 3.4 m.

In recent years, with the development of electronic products toward high frequency and high speed, Printed Circuit Board (PCB) routing technology has been continuously evolving to meet the requirements of complex signal transmission. Meanwhile, the increase in circuit frequency and device density has led to a sharp deterioration of simultaneous switching noise (SSN), which has escalated from a minor interference to a core bottleneck. SSN not only impairs signal integrity and increases bit error rate, but also interferes with circuit operation, causes device failure, and even leads to system collapse, becoming a “fatal obstacle” to the performance and reliability of high-frequency products. The SSN problem has become increasingly severe due to the rise in circuit operating frequency and device density, posing a key challenge in high-speed circuit design. To address the challenge of suppressing SSN at the PCB board level in high-speed digital circuits, this paper proposes a collaborative optimization scheme integrating simulation analysis and the Seagull Optimization Algorithm (SOA). In this study, a multi-physical field coupling model of SSN is established to reveal that SSN essentially arises from the electromagnetic interaction between the parasitic inductance of the power distribution network (PDN) and high-speed transient current. Based on the research on frequency-domain impedance analysis, time-domain response prediction, and decoupling capacitor suppression mechanism, the limitations of traditional capacitor placement in suppressing GHz-level high-frequency noise are overcome. This method enables precise power integrity (PI) design via simulation analysis frequency-domain parameter extraction and power–ground noise simulation quantify PDN impedance characteristics and the coprocessor switching current spectrum; resonance analysis locates key frequency points and establishes an SSN–planar resonance correlation model to guide decoupling design; finally, noise coupling analysis optimizes signal–power plane spacing, markedly reducing mutual inductance coupling. On this basis, the SOA is innovatively introduced to construct a multi-objective optimization model, with capacitor frequency, capacitance value, and package size as variables. A spiral search algorithm is used to balance noise-suppression performance and cost constraints. Simulation results show that this scheme can reduce the SSN amplitude by 37.5%, effectively suppressing the signal integrity degradation caused by SSN and providing a feasible solution for SSN suppression.

To address the challenge of detecting internal defects in medium-thick titanium alloy laser welds, a combined simulation and experimental study on ultrasonic testing was conducted. A finite element model employing a 5 MHz shear wave angle transducer for inspecting titanium alloy welds was established. An ultrasonic testing system was developed, incorporating a DPR300 pulser-receiver (JSR Ultrasonics, Pittsford, NY, USA) and an MSO5204 oscilloscope (RIGOL, Suzhou, China), and was calibrated using standard reference blocks. The inspection results for four prefabricated internal defects at various depths demonstrated that all defects were effectively detected, with the minimum detectable equivalent defect size reaching 1 mm. The measured signal-to-noise ratio (SNR) averaged 17.6 dB, validating the high sensitivity of the proposed system. The mean absolute error for defect localization was 0.438 mm, achieving a positioning accuracy better than 0.5 mm. This study indicates that the pro-posed method enables effective detection and accurate localization of internal defects in titanium alloy laser welds, providing critical technical support for laser welding quality assessment.