Semi-physical Experiment Research Articles

Multi-Centroid Extraction Method for High-Dynamic Star Sensors Based on Projection Distribution of Star Trail

To improve the centroid extraction accuracy and efficiency of high-dynamic star sensors, this paper proposes a multi-centroid localization method based on the prior distribution of star trail projections. First, the mapping relationship between attitude information and star trails is constructed based on a geometric imaging model, and an endpoint centroid group extraction strategy is designed from the perspectives of time synchronization and computational complexity. Then, the endpoint position parameters are determined by fitting the star trail grayscale projection using a line spread function, and accurate centroid localization is achieved through principal axis analysis and inter-frame correlation. Finally, the effectiveness of the proposed method under different dynamic scenarios was tested using numerical simulations and semi-physical experiments. The experimental results show that when the three-axis angular velocity reaches 8°/s, the centroid extraction accuracy of the proposed method remains superior to 0.1 pixels, achieving an improvement of over 30% compared to existing methods and simultaneously doubling the attitude measurement frequency. This demonstrates the superiority of this method in high-dynamic attitude measurement tasks.

Optimization research on control strategies for photovoltaic energy storage systems considering multi-mode operation

In this paper, a selective input/output strategy is proposed for improving the life of photovoltaic energy storage (PV-storage) virtual synchronous generator (VSG) caused by random load interference, which can sharply reduce costs of storage device. The strategy consists of two operating modes and a power coordination control method for the VSGs. Firstly, a selective VSG input strategy is proposed based on the magnitude of disturbances, a method of offline solving model equation is used for determine the VSG input time. An online method is used for matching the disturbance frequency variations, which enables a selective VSG startup method, allowing the grid to prioritize the utilization of the generator's physical inertia. Secondly, a dynamic VSG exit strategy is developed based on dynamic frequency characteristics to prevent secondary oscillations in the frequency recovery phase of the PV-storage VSG following grid disturbances. This strategy is crucial as grid variations may affect energy storage lifespan and reduce frequency recovery speed. Finally, the proposed approach is validated for correctness and effectiveness through computer simulations and semi-physical experiments using the NI-PXI + LabVIEW platform. Through the above optimization and research, the selective start of VSG is realized, the energy storage life is improved, the capacity of charge and discharge cycles is reduced by 37.82 % compared with the strategy without investment and withdrawal, and the life loss in the secondary charge and discharge process of PV-storage VSG is avoided, which is conducive to frequency recovery.

Single-Image Super-Resolution Method for Rotating Synthetic Aperture System Using Masking Mechanism

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system.

Fault location in high-voltage direct-current grids based on panoramic voltage features and vision transformers

This paper presents a novel fault location method designed for high-voltage direct-current (HVDC) grids equipped with quick-action protections and circuit breakers. The method relies on a very short data window of single-ended voltage signals, which allows the acquisition of necessary measurements before the ultra-fast fault-isolation stage. Through fault analysis of the simulated four-terminal HVDC grid, it is observed that the fault distance induces a unique transient response in the voltage signal. However, due to the complex mathematical representation of loop components and the multiple fault factors involved, a data-driven strategy is adopted for fault distance estimation. Based on this strategy, four signal processing techniques, including Gramian angular field, recurrence plot, Markov transition field, and continuous wavelet transform, are used to extract panoramic voltage features. Additionally, four advanced lightweight variants of vision transformers (ViT), including MobileViT, EfficientFormer, Efficient-Model, and EdgeViT, are considered for constructing the regression model. The optimal combination of panoramic voltage features and regression models for configuring the locator is determined by statistical evaluation. The results of semi-physical experiments based on real-time digital simulators demonstrate that the proposed method, first, can accurately estimate the fault distance, second, possesses robustness against high transition resistance, low sampling frequency, and strong noise interference, and third, is practically feasible.

Transformer-based self-supervised image super-resolution method for Rotating Synthetic Aperture system via multi-temporal fusion

Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system’s rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve the image quality to meet interpretation needs, we first delineate the imaging process of the rotating primary mirror and analyze the characteristics of image degradation based on the system’s imaging mechanism. Then, we propose a dual super-resolution (SR) framework based on Swin Transformer and introduce a self-supervised learning method for jointly training the unified SR network using wavelet fusion. The self-supervised learning method effectively utilizes the spatiotemporal correlation of the information contained in images captured at different rotation directions of the rectangular pupil. Moreover, the attention mechanism in Transformer can adopt a global perspective and utilize content-based interactions between image content and attention weights to model strong long-range dependencies in remote sensing images. This approach significantly enhances image quality along the pupil’s shorter side, consequently yielding superior results. Extensive digital and semi-physical imaging experiments, involving six aspect ratios of the primary mirror, demonstrate that our SR method surpasses state-of-the-art methods. The work in this paper can serve as a valuable reference for future space applications of the RSA technology.

GNSS/5G Joint Position Based on Weighted Robust Iterative Kalman Filter

The Global Navigation Satellite System (GNSS) is widely used for its high accuracy, wide coverage, and strong real-time performance. However, limited by the navigation signal mechanism, satellite signals in urban canyons, bridges, tunnels, and other environments are seriously affected by non-line-of-sight and multipath effects, which greatly reduce positioning accuracy and positioning continuity. In order to meet the positioning requirements of human and vehicle navigation in complex environments, it was necessary to carry out this research on the integration of multiple signal sources. The Fifth Generation (5G) signal possesses key attributes, such as low latency, high bandwidth, and substantial capacity. Simultaneously, 5G Base Stations (BSs), serving as a fundamental mobile communication infrastructure, extend their coverage into areas traditionally challenging for GNSS technology, including indoor environments, tunnels, and urban canyons. Based on the actual needs, this paper proposes a system algorithm based on 5G and GNSS joint positioning, aiming at the situation that the User Equipment (UE) only establishes the connection with the 5G base station with the strongest signal. Considering the inherent nonlinear problem of user position and angle measurements in 5G observation, an angle cosine solution is proposed. Furthermore, enhancements to the Sage–Husa Adaptive Kalman Filter (SHAKF) algorithm are introduced to tackle issues related to observation weight distribution and adaptive updates of observation noise in multi-system joint positioning, particularly when there is a lack of prior information. This paper also introduces dual gross error detection adaptive correction of the forgetting factor based on innovation in the iterative Kalman filter to enhance accuracy and robustness. Finally, a series of simulation experiments and semi-physical experiments were conducted. The numerical results show that compared with the traditional method, the angle cosine method reduces the average number of iterations from 9.17 to 3 with higher accuracy, which greatly improves the efficiency of the algorithm. Meanwhile, compared with the standard Extended Kalman Filter (EKF), the proposed algorithm improved 48.66%, 35.17%, and 38.23% at 1σ/2σ/3σ, respectively.

Four-axis dual-band error avoidance attitude measurement algorithm with adaptive active differential interference feature extraction

Infrared radiation attitude measurement technology is a new, non-inertial, satellite independent and fast-responding attitude parameters testing method. However, the random occurrence of infrared radiation during missile flight, such as clouds or solar radiation, can cause significant interference in infrared radiation attitude measurement. To enhance the anti-interference capability of infrared radiation attitude measurement, the adaptive active differential (AAD) interference feature extraction algorithm and the four-axis error avoidance (FEA) attitude calculation algorithm with dual-band infrared radiation (DIR) are proposed. Initially, the long-wave and mid-wave infrared radiation laws of clouds and solar radiation are analyzed to identify their different characteristics in both wavebands. The interference type encountered by the rotating missile during flight is extracted by the designed AAD algorithm. Subsequently, based on the results of AAD algorithm, the adjacent axis long-wave or mid-wave infrared sensor data with less or no interference influence is selected. Finally, the attitude angle information is obtained by using two-axis or four-axis attitude calculation algorithm. The results of the semi-physical experiments indicate that the four-axis dual-band infrared attitude measurement device using the FEA algorithm can achieve RMSE about 1 even under the interference of solar or clouds cover.

A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

Image fusion for the novelty rotating synthetic aperture system based on vision transformer

Rotating synthetic aperture (RSA) technology offers a promising solution for achieving large-aperture and lightweight designs in optical remote-sensing systems. It employs a rectangular primary mirror, resulting in noncircular spatial symmetry in the point-spread function, which changes over time as the mirror rotates. Consequently, it is crucial to employ an appropriate image-fusion method to merge high-resolution information intermittently captured from different directions in the image sequence owing to the rotation of the mirror. However, existing image-fusion methods have struggled to address the unique imaging mechanism of this system and the characteristics of the geostationary orbit in which the system operates. To address this challenge, we model the imaging process of a noncircular rotating pupil and analyse its on-orbit imaging characteristics. Based on this analysis, we propose an image-fusion network based on a vision transformer. This network incorporates inter-frame mutual attention and intra-frame self-attention mechanisms, facilitating more effective extraction of temporal and spatial information from the image sequence. Specifically, mutual attention was used to model the correlation between pixels that were close to each other in the spatial and temporal dimensions, whereas long-range spatial dependencies were captured using intra-frame self-attention in the rotated variable-size attention block. We subsequently enhanced the fusion of spatiotemporal information using video swin transformer blocks. Extensive digital simulations and semi-physical imaging experiments on remote-sensing images obtained from the WorldView-3 satellite demonstrated that our method outperformed both image-fusion methods designed for the RSA system and state-of-the-art general deep learning-based methods.11Dataset is available at https://github.com/sherlockyusun/RSA-Fuse.

Virtual Inertia Implemented by Quasi-Z-Source Power Converter for Distributed Power System

This paper proposes a novel virtual inertia control strategy for distributed power systems with high penetration of renewable energy sources. The strategy uses a quasi-Z-source power converter to emulate the inertia response of a synchronous generator by regulating the DC-link capacitor voltage in proportion to the grid frequency deviation. This paper analyzes the effect of inertia on the frequency regulation of a single-area power system and derives the parameter design method and limitations of the virtual inertia. The paper also introduces the working principle and modulation technique of the quasi-Z-source power converter and presents the virtual inertia control scheme based on a voltage-frequency controller. The paper verifies the feasibility and effectiveness of the proposed strategy through MATLAB/Simulink simulations and dSPACE semi-physical experiments. The results show that the proposed strategy can reduce the frequency deviation and rate of change of frequency (RoCoF) by 20% and 50%, respectively, under load disturbances. The paper demonstrates that the quasi-Z-source power converter can provide flexible and adjustable virtual inertia for distributed power systems without additional energy storage devices.

Data‐driven consistent control with data compensation for a class of unknown nonlinear multiagent systems with constraints

AbstractTo solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment.

Modification method of the infrared attitude measurement model for projectiles based on odd-power functions

The attitude measurement method based on infrared is a new type of projectile autonomous attitude measurement method, which has attracted more and more researchers. However, the attitude measurement mathematical model currently used for projectiles is the simplified model after fitting. To improve the accuracy of the infrared attitude measurement theoretical model and the subsequent attitude calculation accuracy, a modified model of the infrared attitude measurement for projectiles is put forward based on the odd-power functions. After analyzing the infrared attitude measurement model for projectiles, the infrared radiance between the sky and the earth at different observation angles is obtained by using the MODTRAN simulation software. And the odd-power functions are introduced to modify the infrared attitude measurement model to approach the infrared radiance curve. Finally, the accuracy of the current and the modified infrared attitude measurement models are compared through the semi-physical experiments, which indicates that the modified infrared attitude measurement model has the smaller Sum of Squares due to Error (SSE) and the Root Mean Square Error (RMSE) of 1.0686 and 0.0612 and the bigger R-square of 0.9985. The results of this study provide the theoretical basis for the reduction of the projectile infrared attitude measurement error from the root.

Research on Low-Frequency Stability under Emergency Power Supply Scheme of Photovoltaic and Battery Access Railway Traction Power Supply System

Photovoltaics and batteries can be connected to a traction power supply system through a railway power conditioner (RPC) to switch between different control strategies. This can address power quality issues or provide emergency traction for locomotives that unexpectedly lose power and even break through traditional energy barriers in the railway field, achieving a low-carbon power supply for railway energy, and a mutual backup with substations. However, methods to coordinate the control strategies of PV and the battery locomotive traction have not been clearly revealed, nor has the actual stability of the system. In this study, to address the above issues, an emergency power supply scheme is proposed for the first time that utilizes a dual-mode RPC inverter combined with a coordinated control strategy for the PV and battery, achieving the traction of locomotives. In addition, a one-dimensional impedance model was established for the PV system, battery system, locomotive (CRH3), and RPC projected onto the dq coordinate system, and the critical amplitude margin (CAM) was defined to quantitatively analyze the sensitivity and laws of different parameters concerning the low-frequency stability of the system. At the same time, impedance ratios and passive criteria were used to reveal the stability mechanism, and parameter adjustment criteria and design suggestions were put forward. Finally, the feasibility of the emergency power supply scheme of the “PV–battery locomotive network” coupling system and the correctness of the low-frequency stability study were verified using the Starsim semi-physical experiment platform.

Research on adaptive normalization method for attitude measurement of rotating projectile based on four-axis infrared

In view of the problems that the current attitude measurement method of rotating projectile based on three-axis infrared needs to normalize the sensor according to the surrounding environment in advance, resulting in the troublesome application of infrared attitude measurement method and insufficient environmental adaptability, an adaptive normalization method for attitude measurement of rotating projectile based on four-axis infrared is proposed. Based on the mathematical relationship between four-axis infrared sensors, an adaptive algorithm for the output amplitude and offset of infrared sensors is designed, which can realize the adaptive normalization of the environment of infrared sensors at any time. By building the semi-physical experiment and changing the surrounding environment, it is proved that the adaptive normalization method of rotating projectile attitude measurement based on four-axis infrared is effective, which provides convenience for the use of infrared attitude measurement method and further improves the infrared attitude measurement theory.

Design of Two-Degree-of-Freedom Fractional-Order Internal Model Control Algorithm for Pneumatic Control Valves

In response to the problems of the inaccurate pneumatic control valve model, the slow valve position control, and the low precision in the industrial control process, some improvement methods are proposed. Firstly, the fractional-order concept is introduced based on the first-order inertia model and IBBO (improved biogeography-based optimization) is used for iteration to obtain a specific transfer function model. Secondly, a fractional-order and two-degree-of-freedom combined internal model control algorithm is proposed. Finally, semi-physical experiments are carried out on a semi-physical experimental platform. The results show that in the field of pneumatic regulating valves, the fractional-order model has good adaptability and effectiveness, and the two-degree-of-freedom fractional-order internal model control algorithm also effectively improves the accuracy, speed, and robustness of the valve position control.

A Full-Frequency Angular Rate Measurement Method of Spacecraft Based on Multi-MSCSG Weight Assignment

In order to realize the full-frequency angular rate measurement of spacecraft based on Magnetically Suspended Control and Sensing Gyroscope (MSCSG), a full-frequency angular rate measurement method of spacecraft based on multi-MSCSG weight assignment is proposed in this paper. Firstly, the main structure of MSCSG is introduced. The traditional angular rate sensing principle of MSCSG is analyzed on the basis of the working principle of Lorentz magnetic bearing, the key component of MSCSG; After introducing and analyzing the shortcomings of the two existing measurement methods, namely, four-cross configuration and virtual gyro technology, the weight function of frequency is obtained by deriving the measurement accuracy model of the two methods. The weight assignment measurement method is proposed to achieve the fusion of the advantages of the two methods and achieve high-precision angular rate measurement of spacecraft in the full- frequency band. Through simulation and semi physical experiments, we verified that the angular rate accuracy of spacecraft measured by the proposed one can be maintained above 98% at different frequencies, and the higher the frequency is, the higher the angular rate measurement accuracy of spacecraft is.

A Polar Integrated Alignment Assisted by DVL Under Large Azimuth Misalignment

The gravity tends to coincide with the earth’s rotation vector in the polar region, degrading the coarse alignment azimuth of the strapdown inertial navigation system (SINS) to a large misalignment. Under a large azimuth misalignment, the linear error model inevitably renders unacceptable theoretical errors in the integrated alignment. In addition, polar region challenge such sensors as a star sensor of celestial navigation, which fails to provide high-precision attitude reference for integrated alignment. Therefore, in allusion to the misalignment mentioned above based on SINS for the polar region, an integrated alignment algorithm is proposed, assisted by the Doppler Velocity Log (DVL). Notably, the work first investigates the extent to which azimuth accuracy deteriorates in the polar region. On the premise of large azimuth misalignment, the nonlinear error model under the transverse frame is derived from the ellipsoid earth model. Additionally, the state and measurement models are constructed, combined with the velocity of DVL as an observation. An adaptive measurement variance matrix is designed for the unscented Kalman filter (UKF) to estimate the attitude error. Eventually, the semi-physical experiments demonstrate that the proposed algorithm significantly improves the initial alignment accuracy of the polar region.

A novel optimization method for geological drilling vertical well

Geological drilling is an important means for exploration of the earth. Due to the nonlinearity and coupling, geological drilling is accompanied by low efficiency and safety, and difficult to accurately predict the drilling states. Moreover, the vertical well trajectory needs to follow the plumb line. A novel optimization method is proposed to solve these problems. First, working conditions are identified by fuzzy C-means clustering method and corresponding adjustment ranges of operating variables are refined for optimization. Meanwhile, support vector regression and hybrid bat algorithm are employed to construct reliable models for mud pit volume (MPV) and rate of penetration (ROP). Then, how to improve safety and efficiency is converted to a two-objective problem, that is to suppress MPV fluctuations and improve ROP with vertical well constraints. Nondominated sorting genetic algorithm II is invoked to solve this problem, and the appropriate solution is selected by two references points. Moreover, a short-time scale and long-time scale optimization strategy is introduced to cope with different adjustment interval of operating variables. Finally, the simulation results based on actual drilling data, application results in a semi-physical experiment system and comparison results from a vertical well verify the effectiveness and practicability for the developed method.

A Spatial–Temporal Joint Radar-Communication Waveform Design Method with Low Sidelobe Level of Beampattern

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees of freedom. However, a high sidelobe level (SLL) in the beampattern reduces energy efficiency and expands exposure probability. In this study, we propose a novel spatial–temporal joint waveform design method based on the beamforming algorithm to form a low SLL beampattern. Waveform synthesis constraints are considered to synthesize desired radar and communication waveforms at designated directions. Furthermore, we impose the constant modulus constraint to lessen the impact of the high peak-to-average ratio (PAPR). The optimization process of the whole model can be summarized as two stages. First, the covariance matrix is created by convex optimization with respect to the minimum SLL. Second, the integrated transmit waveform is tuned through an alternating projection algorithm. Based on the simulation findings, we demonstrate that the proposed method outperforms the traditional methods in terms of low SLL and waveform synthesis. Meanwhile, we validate the effectiveness of the proposed method using semi-physical experiment results.

Imaging Simulation Method for Novel Rotating Synthetic Aperture System Based on Conditional Convolutional Neural Network

The novel rotating synthetic aperture (RSA) is a new optical imaging system that uses the method of rotating the rectangular primary mirror for dynamic imaging. It has the advantage of being lightweight, with no need for splicing and real-time surface shape maintenance on orbit. The novel imaging method leads to complex image quality degradation characteristics. Therefore, it is vital to use the image quality improvement method to restore and improve the image quality to meet the application requirements. For the RSA system, a new system that has not been applied in orbit, it is difficult to construct suitable large datasets. Therefore, it is necessary to study and establish the dynamic imaging characteristic model of the RSA system, and on this basis provide data support for the corresponding image super resolution and restoration method through simulation. In this paper, we first analyze the imaging characteristics and mathematically model the rectangular rotary pupil of the RSA system. On this basis, combined with the analysis of the physical interpretation of the blur kernel, we find that the optimal blur kernel is not the point spread function (PSF) of the imaging system. Therefore, the simulation method of convolving the input image directly with the PSF is flawed. Furthermore, the weights of a convolutional neural network (CNN) are the same for each input. This means that the normal convolutional layer is not only difficult to accurately estimate the time-varying blur kernel, but also difficult to adapt to the change in the length–width ratio of the primary mirror. To that end, we propose a blur kernel estimation conditional convolutional neural network (CCNN) that is equivalent to multiple normal CNNs. We extend the CNN to a conditional model by taking an encoding as an additional input and using conditionally parameterized convolutions instead of normal convolutions. The CCNN can simulate the imaging characteristics of the rectangular pupil with different length–width ratios and different rotation angles in a controllable manner. The results of semi-physical experiments show that the proposed simulation method achieves a satisfactory performance, which can provide data and theoretical support for the image restoration and super-resolution method of the RSA system.