Search Results (211)

Optical sensor systems are essential for space target detection. However, previous studies have prioritized detection accuracy over model efficiency, limiting their deployment on resource-constrained sensors. To address this issue, we propose the lightweight space target real-time detection transformer (STar-DETR), which achieves a balance between model efficiency and detection accuracy. First, the improved MobileNetv4 (IMNv4) backbone network is developed to significantly reduce the model’s parameters and computational complexity. Second, group shuffle convolution (GSConv) is incorporated into the efficient hybrid encoder, which reduces convolution parameters while facilitating information exchange between channels. Subsequently, the dynamic depthwise shuffle transformer (DDST) feature fusion module is introduced to emphasize the trajectory formed by space target exposure. Finally, the minimum points distance scylla intersection over union (MPDSIoU) loss function is developed to enhance regression accuracy and expedite model convergence. A space target dataset is constructed, integrating offline and online data augmentation techniques to improve robustness under diverse sensing conditions. The proposed STar-DETR model achieves an AP0.5:0.95 of 89.9%, successfully detecting dim and discontinuous streak space targets. Its parameter count and computational complexity are reduced by 64.8% and 41.8%, respectively, highlighting its lightweight design and providing a valuable reference for space target detection in resource-constrained optical sensors. Full article

In this study, square–star-shaped leaf-like BiVO₄ nanomaterials were successfully synthesized using a conventional hydrothermal method. The microstructure, elemental composition, and gas-sensing performance of the materials were thoroughly investigated. Morphological analysis revealed that BiVO₄ prepared at different reaction temperatures exhibited square–star-shaped leaf-like structures, with the most regular and dense structures formed at 150 °C, exhibiting a large specific surface area of 2.84 m²/g. The response performance of the BiVO₄ gas sensors to different target gases was evaluated, and the results showed that the prepared BiVO₄ gas sensor exhibited a strong response to NH₃. At the optimal operating temperature of 300 °C, its sensitivity to 5 ppm NH₃ reached 13.3, with a response time of 28 s and a recovery time of 16 s. Moreover, the gas sensor exhibited excellent repeatability and anti-interference performance. These findings indicate that square–star-shaped leaf-like BiVO₄ holds great potential in environmental monitoring and industrial safety detection, offering new insights for the development of high-performance gas sensors. Full article

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. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

With the rapid advancement of aerospace technology, the maneuverability of spacecraft has increasingly improved, creating a pressing demand for star sensors with a high attitude update rate and high precision. Star identification, as the most complex and time-consuming algorithm of star sensors, faces stringent requirements for enhanced identification speed and an enhanced identification rate. Furthermore, as the space environment is becoming more complex, the need for star sensors with heightened detection sensitivity is growing to facilitate real-time and accurate alerts for various non-cooperative targets, which has led to a sharp increase in the number of high-magnitude navigation stars in the star catalog, significantly impeding the speed and rate of star identification. Traditional methods are no longer adequate to meet the current demand for star sensors with high identification speed and a high identification rate. Addressing these challenges, a voting-based star identification algorithm using a partitioned star catalog is proposed. Initially, a uniform partitioning method for the star catalog is introduced. Building on this, a navigation feature library using partitioned catalog neighborhoods as a basic unit is constructed. During star identification, a method based on a voting decision is employed for feature matching in the basic unit. Compared to conventional methods, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. The performance of the proposed algorithm is validated through a simulation experiment and nighttime star observation experiment. Experimental results indicate an average identification rate of 99.760% and an average identification time of 8.861 milliseconds, exhibiting high robustness against position errors, magnitude errors, and false stars. The proposed algorithm presents a clear advantage over other common star identification methods, meeting the current requirement for star sensors with high star identification speed and a high identification rate. Full article

A star tracker is a widely used celestial sensor in astronomical navigation systems, which calculates the spacecraft’s high-precision attitude by observing stars in space to obtain several star vectors. Existing star identification algorithms typically require the selection of a specific anchor star (e.g., the nearest neighbor star), using the line connecting the target star and the anchor star as the rotational reference axis to achieve rotation invariance in the star identification algorithm. However, this approach makes the entire identification algorithm overly dependent on the anchor star, resulting in insufficient identification accuracy in cases of excessive positional noise or a high number of false stars. In this paper, we adopt the angles between any neighboring stars and the distances ratio between neighboring stars and the observed star as the initial matching criteria. We then calculate the matching with each navigation star using the accumulated angle in the counterclockwise direction based on this criterion. The navigation star with the highest matching is identified. Unlike other identification algorithms that require selecting the nearest neighbor star which can be easily affected by interference as the rotational reference axis, our method effectively achieves rotation invariance in star identification by leveraging angle information. Therefore, it exhibits better tolerance to positional noise, magnitude noise, and false stars, particularly demonstrating higher robustness against focal length variations. Full article

Traditional celestial navigation mainly utilized the sextant to measure the attitude and the position contour method to calculate and resolve the vessel’s positioning problem, but these methods are not rigorous, having major deficiencies in the positioning accuracy. Currently, the small field-of-view star sensor is becoming the main attitude measurement equipment on vessels, and its measurement accuracy directly affects the vessel positioning results. Aiming at this problem, this research provides a model of small field-of-view star sensor positioning accuracy based on the two-star combination method, and numerical solutions are given. In addition, it focuses on the influence of the measurement error of the star sensor, especially the elevation angle error, on the positioning accuracy of the vessel and gives the star selection strategy for practical application. In particular, the star selection strategy is also applicable to other two-star positioning methods. The results show that the analytical solution is computationally simple and real-time, and the effect of measurement errors on positioning can be minimized by the star selection strategy. This study reveals the error influence mechanism based on the dual-star combination approach, which has significant implications for practical vessel navigation using small-field-of-view star sensors. Full article

Biomimetic vision is a promising method for efficient navigation and perception, showing great potential in modern navigation systems. Optical flow information, which comes from changes in an image on an organism’s retina as it moves relative to objects, is crucial in this process. Similarly, the star sensor is a critical component to obtain the optical flow for attitude measurement using sequences of star images. Accurate information on angular velocity obtained from star sensors could guarantee the proper functioning of spacecraft in complex environments. In this study, an optimized Kalman filtering method based on the optical flow of star images for spacecraft angular velocity estimation is proposed. The optimized Kalman filtering method introduces an adaptive factor to enhance the adaptability under dynamic conditions and improve the accuracy of angular velocity estimation. This method only requires optical flow from two consecutive star images. In simulation experiments, the proposed method has been compared with the classic Kalman filtering method. The results demonstrate the high precision and robust performance of the proposed method. Full article

With the continuous advancement of high-resolution satellite technology, the impact of thermal deformation on the performance of star cameras is becoming more significant, particularly in relation to installation conditions and orbital environments. To address this challenge, an in-depth investigation of the thermal design of a star camera is conducted in this study. The thermal deformation of this camera is evaluated systematically through simulation analysis, thermal balance tests, and on-orbit temperature measurements. In addition, a simulation analysis is used to identify and quantitatively evaluate the thermal deformation error sources that affect the spatial attitude measurement accuracy of the star camera. The results indicate that thermal deformations of the optical system, the mounting surface of the star camera, and the support significantly impact on-orbit measurement accuracy. Ultimately, the limit error attributable to on-orbit thermal deformation is determined to be 0.62″. In the thermal balance experiments, the maximum absolute difference between the test results and the thermal simulation analysis results is under 1.8 °C. Additionally, analysis of the orbital temperature data reveals that the maximum absolute difference between the orbital results and the thermal simulation results is 1.18 °C, while the attitude accuracy of the star sensor is better than 0.54″. These findings validate the effectiveness of the thermal design and the accuracy of the thermal simulation analysis. The analysis of error sources presented in this paper offers crucial insights for effectively controlling the thermal deformation errors of star cameras and lays the groundwork for optimizing overall thermal design. Full article

The field of view (FOV)-gated optical imaging system can relieve the contradiction between a wide FOV and the effective suppression of sky background radiation, making it particularly suitable for all-time star sensors. The detection capability of this novel optical imaging system during daytime differs significantly from that of traditional optical systems. This paper presents the principle of suppressing sky background radiation through FOV-gated imaging. Subsequently, the detection capabilities, including detectable limiting stellar magnitude and the probability of detecting at least three stars, are analyzed for applications on airborne platforms operating at altitudes of no less than 3km. Based on the analysis results, an FOV-gated imaging system operating in the shortwave infrared wavelength band was designed. Additionally, stray light analysis software, ASAP, was employed to simulate the illumination of stellar signals and sky background radiation on the detector. The evaluation of the detection capability of the designed FOV-gated optical system, based on simulation data, aligns with the theoretical analysis value. It demonstrates the system’s ability to detect multiple stars with a high probability during the daytime, thereby providing a theoretical foundation for the practical application of the FOV-gated optical imaging system on airborne platforms. Full article

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

Gyros/star sensor integration provides a potential method to obtain high-accuracy spatial orientation for turntable structures. However, it is subjected to the problem of accuracy loss when the measurement noises become non-Gaussian due to the complex spatial environment. This paper presents an event-driven maximum correntropy filter based on Cauchy kernel to handle the above problem. In this method, a direct installation mode of gyros/star sensor integration is established and the associated mathematical model is derived to improve the turntable’s control stability. Based on this, a Cauchy kernel-based maximum correntropy filter is developed to curb the influence of non-Gaussian measurement noise for enhancing the gyros/star sensor integration’s robustness. Subsequently, an event-driven mechanism is constructed based on the filter’s innovation information for further reducing the unnecessary computational cost to optimize the real-time performance. The effectiveness of the proposed method has been validated by simulations of the gyros/star sensor integration for spatial orientation. This shows that the proposed filtering methodology not only has strong robustness to deal with the influence of non-Gaussian measurement noise but can also achieve superior real-time spatial applications with a small computational cost, leading to enhanced performance for the turntable’s spatial orientation using gyros/star sensor integration. Full article

Shack–Hartmann wavefront sensors (SHWFSs) are crucial for detecting distortions in adaptive optics systems, but the accuracy of wavefront reconstruction is often hampered by low guide star brightness or strong atmospheric turbulence. This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. Comparative evaluations using simulated SHWFS light intensity images and corresponding deformable mirror command vectors demonstrate the robustness and accuracy of the Vision Transformer under various guide star magnitudes and atmospheric conditions, compared to convolutional neural networks (CNNs), represented in this study by Residual Neural Network (ResNet), which are widely used by other scholars. Notably, normalization preprocessing significantly improves the CNN performance (improving Strehl ratio by up to 0.2 under low turbulence) while having a varied impact on the Vision Transformer, improving its performance under a low turbulence intensity and high brightness (Strehl ratio up to 0.8) but deteriorating under a high turbulence intensity and low brightness (Strehl ratio reduced to about 0.05). Overall, the Vision Transformer consistently outperforms CNN models across all tested conditions, enhancing the Strehl ratio by an average of 0.2 more than CNNs. Full article

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 dB for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 dB to 16 dB, respectively. Full article

This paper reports the successful application of a self-developed, miniaturized, low-power nano-star tracker for precise attitude measurement of a 5-m-long satellite extension boom. Such extension booms are widely used in space science missions to extend and support payloads like magnetometers. The nano-star tracker, based on a CMOS image sensor, weighs 150 g (including the baffle), has a total power consumption of approximately 0.85 W, and achieves a pointing accuracy of about 5 arcseconds. It is paired with a low-cost, commercial lens and utilizes automated calibration techniques for measurement correction of the collected data. This system has been successfully applied to the precise attitude measurement of the 5-m magnetometer boom on the Chinese Advanced Space Technology Demonstration Satellite (SATech-01). Analysis of the in-orbit measurement data shows that within shadowed regions, the extension boom remains stable relative to the satellite, with a standard deviation of 30′′ (1σ). The average Euler angles for the “X-Y-Z” rotation sequence from the extension boom to the satellite are [−89.49°, 0.08°, 90.11°]. In the transition zone from shadow to sunlight, influenced by vibrations and thermal factors during satellite attitude adjustments, the maximum angular fluctuation of the extension boom relative to the satellite is approximately ±2°. These data and the accuracy of the measurements can effectively correct magnetic field vector measurements. Full article