To improve the shortcomings of the current landing cushion device that requires high landing terrain and is not reusable, a reusable four-legged lander based on a bionic structure is designed with reference to the composition of insect landing leg structures in nature. The support leg adopts the composition of “rigid body control thigh structure plus circular arc structure leg structure”, using a friction brake as a design method for a cushioning and energy absorbing device, which makes the landing impact load as thermal energy by friction work. The friction brake is applied to dissipate the landing impact load in the form of thermal energy to achieve cushion energy absorption. The D-H parametric method is applied to derive the chi-square matrix transformation of the support leg, and the forward and reverse kinematics analysis of the support leg is carried out for the bionic leg type cushioning structure. The theoretical model of the support leg is established and dynamics simulation is performed. The result shows that the reusable lander has good landing buffer performance and can provide a feasible solution for China’s subsequent deep space exploration missions.

In response to the problem of the unmeasurable aerodynamic parameters in the dynamic simulation of parachute recovery systems, a six-degree-of-freedom dynamics equation and kinematic equation were established for the stable descent phase of the parachute system. The form of aerodynamics and the parameters to be identified were defined. In the foundation above, two aerodynamic parameter identification schemes based on BP neural networks were employed. These schemes involved training the neural networks with flight state data until convergence, resulting in the identification of the aerodynamic parameter model to be discerned. The effectiveness and accuracy of the two identification schemes are verified through simulation examples. The identification results of aerodynamic parameters are obtained separately, and performance evaluation metrics are calculated. The simulation results are analyzed in terms of convergence speed, identification accuracy, and other aspects, indicating that both identification schemes exhibited good agreement between predicted results and expected results. However, the double BP neural network method demonstrated superior performance. The findings of this study demonstrate the potential applicability of BP neural network methods in the identification of experimental data in future engineering applications.

Aiming at the problems of safety and attitude stability in the process of air separation of the internal load, the dynamic nested mesh combined with penalty function is applied to simulate the load separation process under the consideration of adapter collision in this paper. Compared with previous studies, this method transforms the aerodynamic characteristics of separation process from discrete steady-state calculation to continuous transient calculation and the calculation of adaptor collision is transformed from feature point method to node-surface method and the coupling solution of unsteady flow field and multi-body collision is realized, which has better fidelity. In this paper, the accuracy of the numerical method is verified by comparing it with the test of standard model in the wind tunnel laboratory of Arnolds Engineering Development Center of US Air Force. Then, the simulation model of load exit system is established and the influence of initial velocity and initial angle of attacks on the process of load exit under the action of towing parachute is analyzed. The results show that the load can safely and stably exit the cabin under standard working conditions; The lower the initial speed, the smoother the separation process, the lower the compression degree of the adapter, when the Mach number is 0.5, the minimum distance between the vehicle and the load increases by 1mm compared with that when the Mach number is 0.8. The larger initial angle of attacks has an adverse effect on the safety and stability of the separation. When the Angle of attack is 15°, the adapter friction is 2.67 times that when the Angle of attack is 5°. The method is suitable for the numerical simulation of the load removal system using the adapter as the safety protection measure, and the conclusion can provide some reference for the study of the separation process of the load removal system.

The temperature of electronic equipment in the payload compartment of an airship greatly affects its operational reliability. As the application scope of the airship payload compartment expands, the total power of the payload continues to increase, leading to significant increases in equipment design power consumption and heat flux density. However, the density of the atmosphere in the stratosphere is only 1/18 of that at the ground level, resulting in poor convective heat dissipation capability. Heat dissipation has gradually become a key issue restricting technological development. To address the thermal pain points under high payload power, this article introduces the two-phase fluid loop heat dissipation method and explores its heat dissipation capability compared to fan-forced heat dissipation. Based on the analysis of the heat transfer characteristics of electronic equipment in the payload compartment, a fan-forced convection heat dissipation system is designed based on computational fluid dynamics. Simultaneously, a method for using a 1,1,1,2-tetrafluoroethane (R134a) fluid loop to dissipate heat in the stratospheric payload compartment for electronic equipment is proposed, and its simulation is conducted using the apparent heat capacity method. The temperature and flow fields of the airship payload compartment under the two heat dissipation methods are calculated, obtaining the heat dissipation capabilities of the two methods. Simulation results indicate that the heat dissipation limit of the fan is approximately 591 W, while the two-phase fluid loop heat dissipation method can meet the heat dissipation requirement of 700 W, thus fulfilling the basic heat dissipation needs. The use of the two-phase fluid loop heat dissipation method can effectively control the temperature of electronic equipment in stratospheric airship payload compartments, providing insights into the design and calculation of heat dissipation for high-power electronic equipment in the stratosphere.

With the increasing of spacecrafts going to the moon, the detection capability of the existing near-Earth space optical satellites and ground-based optical telescopes can no longer meet the observation requirements, and it is urgent to enhance the cislunar space observation capability. It is considered as an effective means to make up the deficiency of cislunar space observation capability for observations using the cislunar periodic orbit. According to the characteristics of the cislunar periodic orbit, the orbit is modeled based on the principle of differential correction and the algorithm is designed. An orbit selection principle is proposed and three optimized orbits are given. In the analysis of the visibility of the cislunar periodic orbit to the Halo Orbit near the cislunar Lagrange points L1 and L2, in order to facilitate the evaluation, the spheroid-like object magnitude evaluation model is used to define the evaluation criteria of visibility. Based on the spatiotemporal relationship between natural objects, object and remote sensing satellites involved in the calculation of the model provided by the satellite toolkit, the visibility analysis process and results are presented. The results show that the cislunar periodic orbit has a good observation effect on the Halo Orbit near the cislunar lagrange points L1 and L2, and the visibility of the cislunar periodic orbit with serial number 1 is the highest, which is greater than 95%.

Traditional brightness-based spatial target size inversion methods require the combination of detector response, and the response error can affect the accuracy of the inversion. This paper proposes a method that introduces cooperative target brightness information to assist in size inversion. The brightness information of cooperative targets is compared with spatial targets to eliminate the detector response with larger errors and then is transformed into more accurate position information of cooperative targets, thereby improving the accuracy of size inversion. This paper first establishes a spatial target observation model based on the brightness and position information of cooperative targets. Then based on the radiometry, the analytical formula for spatial target size inversion is derived, and the calculation error is given. Finally, the model is validated with an example. The result shows that the size inversion accuracy based on cooperative target brightness information is improved by about 46.9% compared with traditional methods.

Aiming at the system selection and design scheme of remote sensing stereo mapping satellites, combined with the current status of space mapping capability, this paper proposes a multi-baseline space mapping model by analyzing the advantages and bottlenecks of the traditional large, small and double baseline aspect ratio mapping models, to adapt to the mapping tasks of different terrain conditions such as mountain buildings. According to the theoretical basis for the derivations of factors affecting positioning accuracy under the stereoscopic imaging model, a numerical simulation scene is constructed, and the relationship between different matching errors and the plane and elevation accuracy is obtained by combining the analytical aerial triangulation method. The simulation results show that for different degrees of matching errors, the smaller ratio of baseline to altitude of about 0.15 to 0.6 is generally suitable for terrain characteristics with high requirements for the plane accuracy, and the larger larger ratio of baseline to altitude of about 0.8 to 1.2 is suitable for terrain characteristics with high requirements for the elevation accuracy. Accurate selection of the ratio of baseline to altitude according to the corresponding terrain can improve the positioning accuracy. The research results of this paper provide a reference for guiding the design of satellite mapping capability improvement schemes.

Accompanying the demands for laser communication network integration, lightweighting, and the widespread adoption of transceiver integration, achieving efficient isolation between optical antenna transmission and reception is crucial. This article thus investigates the isolation between coaxial and off-axis antennas. Initially, using Code V optical design software, coaxial antennas and off-axis two-reflective antennas operating in the 1 550 nm band were individually designed with a field of view angle of ±1.5 mrad, approaching the diffraction limit. Subsequently, based on the stray light scattering model, ray tracing was conducted using analysis software to simulate isolation. Methods were then studied to place baffles at the center of coaxial antenna secondary mirrors to mitigate aperture risk, and to adjust off-axis tilt and curvature radii in off-axis antennas. Ultimately, the isolation levels of coaxial and off-axis antennas were respectively increased to -69 dB and -89 dB. The results indicate that the aforementioned methods effectively enhance the isolation of optical antennas.

In order to solve the on-orbit temperature drift problem of TDI-CMOS sensors and further improve the imaging quality of the space camera, it is necessary to control the temperature rise of the sensor chip during on orbit imaging, and optimize the heat transfer path of the focal plane assembly by using a phase change energy storage device. Firstly, the simulation and experimental boundary conditions are determined based on the thermal flow environment of the track and the working characteristics of the focal plane assembly. The existing focal plane assembly structure is used for modeling, simulation analysis and thermal testing to obtain the temperature results of each part before optimization. Then, based on the existing structure, thermal control optimization design is carried out to study the effect of different heat transfer paths on the temperature improvement of CMOS sensors, and a thermal design scheme of the focal plane assembly supplemented by a phase change energy storage device is determined, Finally, the thermal equilibrium test of the focal plane assembly is carried out to verify the correctness of the design scheme. The experimental results show that the enhanced heat transfer method combining the pin heat dissipation scheme and side heat dissipation can meet the temperature rise requirements of CMOS sensors within 5 ℃ during operation, and the DN value change is less than 0.2%. The optimized scheme effectively improves the imaging quality.

With the continuous development of the field of remote sensing, space-based remote sensing is developing in the direction of all-sky and intelligent. Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and morning and night periods, it results in the characteristics of low contrast, low brightness and low signal-to-noise ratio of remote sensing images, among which, low signal-to-noise ratio leads to a large number of complex physical noises drowning the image features, seriously affecting the recognition and interpretation of ground objects. This paper summarizes the actual full-link physical model based on optical remote sensing imaging and the technical approaches to improve the signal-to-noise ratio of remote sensing images, and summarizes the methods based on traditional filtering, physical model and deep learning respectively. By comparing the differences among the main representative algorithms of various methods, the paper summarizes their respective characteristics. The future development direction of the improvement of the signal-to-noise ratio of space-based remote sensing images is forecasted.

Remote sensing camera image transmission interface performance is an important indicator that affects the overall performance of the camera. In order to improve the circle image data transmission rate and at the same time to meet the needs of lightweight and miniaturization for multi-channel cameras, this article proposes a multi-channel high-speed image data transmission scheme with high throughput and low bit error rate for small and medium-sized cameras. The scheme is based on CoaXPress interface using the Aurora 8B/10B communication protocol, and downstream high-speed image transmission is achieved through the FPGA high-speed serial transceiver GTX for four sets of focal plane components, with the test data rate up to 13 Gbps and the upstream camera control data rate of 21 Mbps. Simulation and test results show that the scheme greatly improves the camera’s image data transmission rate and has a nearly 50% reduction in the interface cable number compared with the traditional methods, which can meet the demand for multi-channel high-speed image transmission of light and small remote sensing cameras, and provide a new solution for the high-speed miniaturization of various types of remote sensing cameras.

In this paper, a single direction quantitative image quality improvement method based on on-orbit MTF test is proposed to improve the image quality due to the phenomenon that the image quality of the TDI push-sweep optical remote sensing camera may decrease in along-track or vertical track. Firstly, the MTF curves in both directions were tested by the edge bar method. N different frequency points in the range from zero frequency to Nyquist frequency were selected to determine the MTF improvement multiple of each selected frequency point according to the ratio of the MTF target value to the measured value. The spatial convolution function was constructed according to the frequency domain response characteristics and the lift multiple was taken as the amplitude of the frequency domain response. At the same time, the noise suppression function is constructed based on the on-orbit measured signal-to-noise ratio (SNR), and the quantitative improvement of MTF is realized under the premise of ensuring SNR. According to the verification results of the on-orbit test, the image quality is improved according to the proposed method. Within the noise suppression threshold of 0.5 dB, the MTF of the vertical rail direction is basically unchanged, the MTF of the Nyquist frequency point along the rail direction is increased by 2.51 times, and the clarity of the obtained image is improved by 8.33%, which proves that the method can effectively achieve quantitative image quality improvement.

This paper proposes a ship detection network called the Long and Short path Feature Fusion Network (LSFF-Net) to address the challenges of detecting small and inshore samples in SAR image ship detection tasks. In LSFF-Net, the Long and Short path Feature Fusion Block (LSFF-Block) makes the model compatible with different scale target information. The application of structural re-parameterization enriches the module learning ability, and the multi-scale features are fused with the feature pyramid network. To address inshore target detection, a data redistribution algorithm is designed to increase detection accuracy of nearshore targets. The experimental results show that the proposed algorithm fully learns the information of the image and is more in line with the characteristics of SAR images. The average precision (AP) of the algorithm reaches 97.50 % in the public data set detection results, which is better than the mainstream target detection algorithm. LSFF-Net provides a new solution for improving the accuracy of small and inshore target detection in SAR images.

Building extraction using remote sensing images plays an important role in urban planning, land use investigation and other fields. However, the buildings in the image are of various types and sizes, which brings great challenges to automatic extraction. In order to solve the problem of voids in large-scale buildings and missing detection in small-scale buildings in remote sensing image extraction, this paper designs a method that combines multi-scale features with non-local computation. The method adopts encoder-decoder structure. Firstly, Res2Net50 is used as the encoder to improve the multi-scale feature extraction capability, and then a non-local computing module is introduced in the decoder part to obtain context information to further improve the extraction results of buildings with different scales. The results indicate that IoU and F1 values of the proposed method on the WHU building dataset reache 89.65% and 94.55%, respectively, , which is 1.52% and 0.86% higher than that of the original UNet and proves the effectiveness of the proposed method.

To address the problems of low detection accuracy, high missed detection rate in small target detection, and low detection efficiency in practical application scenarios in satellite remote sensing image target detection, a multi-scale target detection method based on improved YOLOv7 for satellite remote sensing images is proposed. In the detection network, the focus is on improving the detection capability of small targets by adding a ConvNeXt Block (CNeB) with class attention, which enhances the ability of extraction and utilization of fine-grained features of small targets. At the same time, a post-processing mechanism is proposed to establish the mutual relationship between small and large targets, enabling the detection of multiple-scale targets using a single model. Experimental results show that on four small targets in the TGRS-HRRSD dataset, the improved detection model achieved an average improvement of 16.6% in mean average precision compared to the original YOLOv7. In specific large target detection tasks, the post-processing mechanism reduced the time by 70% compared to YOLT while maintaining accuracy. Compared to mainstream remote sensing image detection methods, this method is more accurate and faster in detecting multi-scale targets.

In order to solve the problem of missing aircraft target maneuver data sets, this paper uses kinematic modeling to generate a rich trajectory data set, which provides necessary data support for network training. In order to solve the problem that it is difficult to establish a kinematic model for trajectory prediction at the current stage and that it is difficult to extract spatiotemporal features with the time series prediction method, an aircraft target trajectory prediction method that combines the Transformer encoder and the Long Short Term Memory network (LSTM) is proposed. It can provide supplementary historical information and attention-based information representation provided by LSTM and Transformer modules at the same time, improving model capabilities. Through comparative analysis with some classic neural network models on the data set, it is found that the average displacement error of this method is reduced to 0.22, which is significantly better than 0.35 of the CNN-LSTM-Attention model. Compared with other networks, this algorithm can extract hidden features in complex trajectories. When facing complex aircraft trajectories with continuous turns and large maneuvers, it can ensure the robustness of the model and improve the accuracy of prediction of complex trajectories.