A positioning method is proposed for high Earth orbit （HEO） spacecraft based on Chebyshev orthogonal domain transformation. By transforming the time-varying receiver coordinates over a period into an invariant Chebyshev coefficient domain， this approach is based on effective combination with sparse ranging observations obtained by the spacecraft across different epochs， which enables joint resolution of multi-epoch measurements. Under conditions of sparse satellite visibility， the historical observation data is leveraged to provide effective constraints for positioning on the current epoch and achieve continuous and reliable positioning for medium-high Earth orbit （MHEO） spacecraft. Furthermore， the Chebyshev based positioning method demonstrates robustness against random measurement errors during observation， which enhances GNSS positioning precision in high-altitude environments. The experimental and simulation results demonstrate that the method is superior to the Extended Kalman Filter （EKF） by handling random noise and surpasses traditional least squares algorithms in both computational speed and positioning precision， which is capable of continuous positioning for spacecraft by pseudorange-level in high Earth orbit scenarios.

To address the issue of safety limitations of traditional path planning algorithms in dense obstacle environments， a Voronoi diagram-based safe obstacle avoidance algorithm is proposed for polygonal obstacle regions. Firstly， a circular coverage model with minimal overflow rate is established to optimally encapsulate obstacle areas. Subsequently， a Voronoi diagram construction algorithm is designed， which is based on the circular coverage to generate a navigable skeleton of the free space. Furthermore， a path generation method integrating unmanned aerial vehicle （UAV） kinematic constraints is developed by using the skeleton. Finally， cubic B-spline interpolation is applied for ensuring path smoothness. The results of simulations demonstrate that， compared with paths generated by an improved A* algorithm， the proposed method achieves smoother trajectories while maintaining comparable path length and significantly increasing the minimum distance to obstacles， and highlighting its superior safety performance in obstacle avoidance. The research can serve as a practical solution for ensuring safe UAV navigation in complex urban environments.

To address the issues of challenges posed by the resource constraints faced by emergency UAVs equipped with communication base stations， a heterogeneous integration architecture and system-level resource constraints optimization research is proposed. Firstly， based on the characteristics of UAV subsystems， a heterogeneous air-space-ground integrated emergency communication framework is constructed. Secondly， specific performance indexes and functional requirements are formulated under resource constraints， and targeted interference mitigation solutions are developed. Finally， through link budget analysis and field experimental validation， the effective coverage range at the recommended transmission rate is empirically determined. By ensuring UAVs which can "fly farther， see clearer， be reliably controlled and be effectively utilized" during critical moments and supporting the sustainable development of the low-altitude economy， the proposed research has points to achieve the goals.

The utilization of drones and other devices for low-altitude inspection is recognized as a typical application scenario in the future low-altitude economy. Infrared imaging， as a critical tool for low-altitude environmental perception， faces challenges in acquiring reliable datasets due to high costs and difficulties in ensuring confidentiality. A style transfer-based target implantation method is proposed to generate simulated infrared images. On the basis of this method， which is initiated by solving the target temperature field through finite element analysis， atmospheric transmission effects are incorporated to render preliminary simulated images. A convolutional neural network-based style transfer technology is then utilized to achieve high-quality implantation between targets and real infrared background images. Comparisons are conducted against traditional methods through three different scenarios. Quantitative evaluations are performed by using objective metrics， including information entropy， peak signal-to-noise ratio， standard deviation， average gradient and spatial frequency. Experimental results demonstrate average improvements by 5.82%， 1.03%， 4.24%， 10.5% and 33.58% of these metrics， respectively. The proposed method is proven to significantly outperform traditional approaches in high-frequency information reconstruction and detail preservation.

Traditional scene matching methods for unmanned aerial vehicles （UAVs） in low-altitude environments often suffer from ineffective outlier rejection， leading to degraded positioning precision. To address this issue， an improved scene matching localization algorithm is proposed in this paper. Firstly， initial data are generated by using triple relationships in this algorithm. Subsequently， a ternary matching optimization method is introduced by combining triangular feature similarity measurement and maximum Euclidean distance screening to reduce computational costs and enhance matching correctness. Furthermore， a data refinement strategy is adopted to improve the sampling performance of the algorithm. Simulation results demonstrate that the proposed algorithm achieves superior accuracy and real-time performance for UAV scene matching localization in complex low-altitude environments， which significantly improves computational efficiency and positioning precision.

To address the issue of challenge of rapid and accurate prediction of the trajectory terminal velocity by using offline trajectory optimization methods during unmanned vehicles operation under complex and strong interference， an integrated velocity prediction and control algorithm is proposed， which is based on the improved gated recurrent Unit neural network algorithm. The velocity prediction is based on the parameters of the neural network model trained by a trajectory data sample library， which takes an eleven-dimensional feature sequence including the target position， current altitude， velocity， ballistic angle and other relevant parameters as input of the network and the velocity at the final moment as the output， and yields a neural network model capable of predicting terminal velocity. Based on the velocity prediction results， a decoupling control scheme for velocity and position is employed for terminal velocity control. The predicted terminal state deviations are used to correct and generate closed-loop control inputs for terminal velocity regulation. In final stage， the designed velocity prediction and control method are validated through six-degree-of-freedom （6-DOF） ballistic simulations. The simulation results demonstrate that accurate and effective velocity prediction and control can be relatively achieved by using proposed algorithm applied to the terminal velocity under 6-DOF closed-loop state.

To address the challenge of significant attitude deviations and persistent oscillations in coaxial unmanned aerial vehicles （UAVs） under wind disturbances， an adaptive backstepping attitude control method is proposed. Firstly， a nonlinear attitude dynamics model integrating with wind disturbances is established through mechanical modeling for coaxial UAVs. Subsequently， a neural network is employed to estimate real-time disturbance amplitudes in the pitch and roll channels for the torque and model uncertainties caused by wind disturbances， while an adaptive backstepping controller dynamically adjusts control parameters for precise stabilization. Finally， the performances of attitude tracking and disturbance resistance of control system are tested through simulations. Comparative simulations demonstrate the adaptive backstepping based method has superior performance over PID control in attitude tracking accuracy and disturbance rejection robustness and significant improvements in overshoot suppression and oscillation attenuation. These results validate this solution in complex disturbance environments for coaxial UAV attitude control.

Aiming at comprehensive optimization of the robust disturbance rejection capability， convergence time， and control accuracy of traditional UAV cooperative formations， a particle swarm optimization-based fast robust cooperative control method for multiple UAVs is proposed in this paper. A finite-time cooperative formation controller is designed to accelerate the response speed of traditional distributed formations. A fast disturbance observer is developed to compensate for the control system， which is capable of accurately estimating composite disturbances within a finite time， thereby the formation control precision and robust disturbance rejection are enhanced. On this basis， by considering both the convergence time and control error， a penalty-based particle swarm optimization algorithm is employed to optimize the design parameters of the formation system， which comprehensively improves the flight performance of multiple UAVs robust cooperative control.

To address the issues of complex modeling procedures of conventional methods for quadrotor UAVs carrying time-varying slung loads and the poor adaptive capability of traditional PID controllers under complex wind disturbances， a Kane's method-based dynamic modeling approach and a model reference adaptive control （MRAC） method with adaptive learning rates are proposed. Kane's method takes advantage of combination of forces and partial velocities， which eliminates the need for explicit analysis of cable constraint forces required by Newton-Euler formulations and bypassing the Lagrangian function established with second-order derivative computations， thereby the calculation process is simplified. The adaptive learning rate's MRAC method enables quadrotor UAVs to resist composite disturbances from wind and time-varying load variations through adaptive learning rates application and variable parameter control， which achieves precise position and attitude control. Simulation results show that under composite disturbances from time-varying loads and complex wind fields， the adaptive learning rate's MRAC demonstrates superior performance in both overshoot suppression and convergence rate compared with conventional MRAC.

To address the challenges of insufficient control stability， limited navigation precision and poor generalization ability encountered by unmanned aerial vehicles during implementation of autonomous visual navigation tasks， a brain-inspired convolutional neural network-spiking recurrent neural network （CNN-SRNN） is proposed to achieve robust end-to-end stable flight navigation strategies with strong generalization capabilities. This network architecture simulates the flight control circuit of the fruit fly brain， which uses CNN network by extracting visual features to form high-level state representations and integrates with an attention mechanism for precise target recognition and localization. A spiking recurrent neural network （SRNN） serves as the flight navigation controller， which realizes time sequence motion information integration and flight control. Additionally， a regularization strategy based on Gershgorin disc theory is designed to enhance the stability of navigation control. Evaluations of UAV navigation performance through diverse simulation environments demonstrate that CNN-SRNN network has the outstanding scene-generalization capability， robustness against noise and decision-making stability. The encoding and decoding relationship between neural activation patterns in SRNN and UAV flight trajectories is further analyzed， and the navigation control mechanisms of brain-inspired neural network are revealed and model interpretability is significantly improved.

Under the background of the vigorous development of theory and application of embodied intelligence， in order to further improve the intelligence level of low altitude UAVs and expand their application boundaries， the development requirements of low altitude UAVs are focused on the context of low altitude economy， and the researches are implemented and systematically analyzed， regarding three domains in terms of the theoretical basis， key technology paths and application challenges. Firstly， the advantages and research value of embodied navigation compared with traditional navigation on semantic understanding， environment interaction and group collaboration are determined. Secondly， derived from the development of traditional simultaneous location and mapping（SLAM） to the technology evolution of visual language navigation（VLN） and visual language action（VLA） model， one special navigation technology framework for low altitude UAVs is established. Thirdly， through analysis of two typical application cases， the practical application trend of low altitude UAV in complex environment is discussed. Finally， future development directions of low altitude UAV embodied navigation are overviewed， the proposed achievement can serve as valuable references for theoretical research and industrial application to intelligent autonomous navigation.

The evolutionary trajectory of aerospace control technology is focused from classical control and modern control to agent-featured intelligent control technology 3.0. The agent-featured intelligent control technology 3.0 is represented and known as the key indicators of future aerospace control systems. The key attributes of control technology 3.0 labelled by "learning while flying"， "lifelong learning" and the "new-generation system architecture" are pointed to elucidate. The critical technologies of "intelligence empowerment"， "functional augmentation" and "information capability enhancement" are subjected to in-depth analysis. On this basis， the exploration of future aerospace intelligent control development is oriented to typical scenarios such as large model empowerment and software factories. Consequently， prospective thoughts on the development of advanced intelligent aerospace control are expanded upon the matter.

With the rapid development of information technology and the significant increasement in product complexity， the cost of physical experiments is still on the rise， so that​simulation technologies are recognized as critical tool for system design optimization. However， traditional simulation algorithms are challenged by high computational resource consumption， difficulty in global optimization and poor adaptability. Regarding these issues， a general simulation algorithm scheduling platform is proposed and based on deep reinforcement learning， which consists of a task list distribution algorithm based on SAC and a computational task scheduling algorithm based on DQN. Tasks are intelligently distributed to computing nodes through the SAC algorithm to optimize task execution efficiency， and but the autonomous scheduling capability of computing nodes is enhanced by the DQN algorithm through experience classification， thereby improving resource utilization. Experimental results demonstrate that the proposed algorithms achieve superior performance by comparing with traditional methods in terms of both task completion time and resource utilization， which validates their effectiveness and advancement.

The generation of abnormal time-series data for weapon systems is one of the core challenges in the field of industrial intelligent operation and maintenance. Traditional equipment data generation method suffers from several limitations in abnormal data generation， including strong coupling in the latent space， violation of physical laws and a lack of diverse abnormal patterns. To address these issues， a causal disentangled VAE method is proposed in this paper， which involves latent variable independence through causal graph constraints， incorporates physical equations taken as prior knowledge in the decoder and a dynamic perturbation strategy employed to generate diverse abnormal data and realize equipment abnormal time-series datasets intelligentized generation. Experiments demonstrate that this method takes advantage of fairly good adaptability by single factor accuracy control， fit time dimension fault propagation and physical constraint controllability， and the CD-VAE significantly outperforms existing methods in terms of physical plausibility， interpretability and the training effectiveness of anomaly detection models on multiple cross-domain public benchmark datasets.

In this paper， the federated contrastive learning method for SOH estimation of lithium batteries is proposed. Firstly， federated learning is utilized to jointly train a model across multiple clients， which can enable knowledge sharing among clients while data privacy is protected. Next， in the federated learning framework， the concept of contrastive learning is introduced to achieve feature alignment among multiple clients， thereby the data distribution differences among clients can be reduced. Further considering the differences in data quality among different clients， a dynamic weighted aggregation algorithm is proposed to reduce the impact of low-quality data on the global model. Finally， the effectiveness of the model is validated on the 18650 lithium battery data set and data privacy protection and the fairly lower SOH estimation error are both guaranteed.

Aiming to the problem that the spin modulated inertial navigation system carried by aerospace vehicles is difficult to achieve accurate initial alignment for short time during cold start up， a new alignment method based on recursive least squares（RLS）and multi-position alignment is proposed. Firstly， this method is based on the velocity error analysis of the inertial navigation system， and the correlation equation is deduced between the error velocity with the attitude misalignment angles and the zero offset of inertial measurement units to realize the error corrections of the inertial navigation system. Next， RLS estimation and iterative optimization are used to analyze the initial target alignment error parameters. Finally， it is verified by simulation experiments and physical experiments. The results show that in the case of cold start in a short time， compared with the traditional Kalman filter alignment algorithm， the initial alignment precision of the azimuth angle of the inertial navigation system can be significantly improved and the average error standard deviation is reduced by about 42.7%.

According to serious flexible oscillation of solar panels， based on the internal mode control structure and the frequency domain secondary performance index， the frequency domain H₂ controller of small satellite attitude is designed for a class of error transfer functions， and the cascade control engineering application mechanism is adopted to add PI regulator to enhance the robustness of the control to the change of model parameters. The simulation results show that the H₂ cascade control is better than PID control in suppressing the flexible oscillation of solar panels and has strong adaptability to the model parameter changes.

A many-to-many rapid rendezvous method based on two-impulse planning is proposed for scenarios involving a spaceborne mother platform deploying multiple sub-satellites for fast rendezvous to engage with multiple target spacecraft. Firstly， a semi-analytical solution method for the single-impulse reachable domain of mobility-constrained spacecraft is presented， which significantly reduce computational complexity. By ensuring the mother platform's reachable domain that covers the target orbit， the required impulsive velocity magnitude is calculated， while the optimal impulsive velocity direction is determined by minimizing the future relative distance between the mother platform and the target spacecraft. Subsequently， a genetic algorithm is employed to optimize the pulse sequence of the mother platform by taking fuel efficiency as the performance metric， and after mother platform maneuver is obtained， the orbital deployment positions is yielded for sub-satellites post-maneuver. Finally， regarding the purpose of shortest time for each sub-satellite， Lambert maneuvers based method is utilized to compute orbital transfer points and target track rendezvous points， which enables rapid multi-target rendezvous. The simulation results demonstrate that the proposed method allows the mother platform to minimize fuel consumption while ensuring the sub-satellites achieve rapid rendezvous with multiple target spacecraft under the shortest time conditions.

According to thrust failure during the orbit insertion phase of a launch vehicle beyond the atmosphere， a trajectory replanning method based on a multi-operator differential evolution algorithm is proposed. On the basis of traditional differential evolution algorithm， chaotic mapping and multi population parallel computing are introduced to improve the slow calculation speed and easy getting stuck in local optimal solutions of traditional methods during dealing with trajectory planning problems. At the same time， a decision vector processing mechanism is specifically designed to address the issues of disorder and repetition of time variables. The experimental results show that the algorithm proposed outperforms the standard deviation evolutionary algorithm in both convergence accuracy and running time. And by comparing with the pseudo-spectral method and iterative guidance algorithem， the effectiveness and accuracy of this algorithm proposed in trajectory planning are further verified， hereby demonstrating its potential for industry applications.

Regarding close-proximity explorations near asteroids， a collision avoidance low-thrust trajectory optimization method is proposed， which integrates Flipped Radau pseudospectral approach with convex optimization. A four-mass gravitational field modeling method is introduced to enhance computational efficiency while precision is maintained in gravitational field calculations. The dynamics is discretized by using the Flipped Radau pseudospectral method and the ellipsoide constraint is imposed to avoid collision， then the trajectory optimization issue is proposed. By applying convexification techniques to reformulate non-convex terms in the issue， and a pseudospectral convex optimization framework is established. The optimal trajectory is obtained by iteratively updating the nominal values until convergence. The collision avoidance is enabled in low-thrust transfer trajectory optimization among equilibrium points near asteroids by using the proposed method. It demonstrates favorable convergence performance and computational efficiency in complex asteroid exploration scenarios， thereby providing theoretical and technical support for asteroid explorations and other deep space exploration missions.

Regarding the challenges associated with hypersonic vehicle flight control， such as difficulties in meeting angle-of-attack （AOA） constraints and the tendency for tracking errors to exceed limits， an asymmetric time-varying constraint backstepping control scheme is proposed， which integrates a fixed-time sliding mode disturbance observer with prescribed performance control. Firstly， the longitudinal dynamic model of the hypersonic vehicle is established， and the velocity and altitude subsystems are identified. These subsystems are then transformed into strict feedback models for backstepping controller design. Subsequently， by combining prescribed performance control with an asymmetric barrier function， a novel control scheme is designed to ensure that the AOA error remains strictly within a predefined time-varying range. Additionally， a fixed-time sliding mode disturbance observer is introduced， which guarantees that the disturbance estimation error converges within a fixed time and ensures the robust performance of system under external disturbances. The simulation results validate the effectiveness of the proposed control scheme which is compared with traditional control methods， and demonstrates superior performance in terms of AOA constraint satisfaction and tracking precision.

Regarding the geosynchronous orbit （GSO） multi-target flyby imaging services mission， a two-layer mission planning method based on a novel orbital configuration is proposed. Firstly， orbital configurations and maneuver strategies applied to existing research are analyzed， and a frozen-high elliptical orbit （F-HEO） is proposed for the mission. Based on J ₂ dynamics model， considering with temporal and positional consistency constraints， the perigee double-impulse maneuver strategy is studied. On the basis of this， the mathematical model for the inner-layer single-target maneuver planning and the outer-layer multi-target sequence planning are established. A dynamic neighborhood search （DNS） is adopted in the inner-layer to accelerate search， and the outer-layer employs a genetic algorithm to obtain the global optimum. Finally， a simulation case is implemented by considering with typical GSO targets.Results verify the feasibility， rationality and accuracy of the mission planning method and show that DNS can effectively reduce the time consumption of inner-layer optimization computations and the F-HEO takes advantage of lower fuel and time costs.

According to liquid sloshing control with pole-zero structure，a method using disturbance compensation is proposed in this paper.The total disturbance torque is estimated by using the extended state observer（ESO） and used as a control command.Based on the typical dynamics model of launch vehicle，the transfer function of the rocket body is derived，and the relationship between the sloshing structure of pole-zero and the disturbance compensation torque is obtained. Though the engineering application， suggestions on the use of the method is presented，and the feasibility of method is proven by a calculation instance，which can serve as reference for stability control of liquid sloshing.

Regarding the online evaluation of the flight capability of aerospace transportation spacecraft， a customized online reachable domain calculation method is proposed. Firstly， a reachable orbit envelope calculation scheme is designed around the current orbit plane under given constraints and initial conditions. Secondly， customized methods and hardware product modules for heterogeneous acceleration are designed， which can quickly achieve online calculation of reachable orbit envelopes and provide fuel optimization planning guidance for specific orbits. Finally， the proposed customized calculation method for reachable domain is validated from the aspects of reachable domain calculation analysis and planning guidance. The results show that the proposed algorithm has good convergence， can quickly calculate the reachable domain envelope and the control variables can smoothly adapt to changes in orbital parameters.

Regarding the focused security requirements of test launch and control system in authentication and authorization， data security protection and traceability of abnormal operations， researches are conducted on the application of blockchain technology in authentication and authorization management. The design of network architecture， account information model， consensus mechanism and differentiated authorization smart contract algorithm are elaborated in this paper， which is verified through the simulation by using self-developed measurement and control network blockchain authentication and authorization platform.

Aiming at full system test data and career information， environmental data， etc of multiple source and huge heterogeneous data of aerospace equipment， a data-based health management system is developed，which achieves the integration and storage management of test data，product career data，environmental data， model analysis and health assessment.The system is developed through service-oriented architecture design， which is based on information gathering and processing for realizing integration and management of equipment test data and takes advantage of high-reliability test data storage and management foundation using lightweight distributed column storage mechanism.Regarding the health status of the system and key units， the trend analysis，life prediction， maintenance decision information and hierarchical situation presentation， which are based on algorithm models，are introduced. Realization of the health status evaluation，health trend analysis，life prediction，maintenance decision support，etc for the full system and key units can be served as a reference of equipment data health management solution.

As a critical factor affecting safety-critical systems， it has been drawn increasingly attention to software safety issues. Based on engineering practices in aerospace embedded software testing and verification， the software safety requirements are taken as a clue and typical safety problems are focused in this paper. The two dimensions are introduced by the formal verification of source code safety properties and the automated testing of software safety requirements which analyze and summary key technologies， including special safety analysis， source code static analysis， source code model checking， fault-model-based safety testing and keyword-driven automated testing. A comprehensive technical solution for aerospace embedded software safety verification is proposed， and an independent controllable software assurance support platform and tools are developed to systematically enhance the trustworthy assurance capabilities of aerospace embedded software.

Aiming at the current situation of tight software-hardware coupling， large scale and high complexity in the test and launch control system of aerospace equipment， a layered decoupled software-defined test and launch control system architecture is proposed. Through taking advantage of middleware technology， the hardware resources of test and launch control system are highly integrated and abstracted， and application-oriented software is enabled to dynamically load and reconstruct components based on users' requirements. The proposed architecture has the features of hardware-on-demand expansion and flexible high extensibility of software， which improves the utilization rate of test and launch control system resources， task flexibility and system reliability.

Aiming at the shortage of existing fault diagnosis methods for in-orbit satellites， a satellite fault diagnosis system based on Clips expert system is proposed in this paper. The satellite fault is diagnosed in real time by taking advantage of existing satellite fault knowledge and experience， combining expert experience with real-time telemetry data and using inference machine technology. At the same time， expert knowledge is input and edited by visualization technology including graphics and knowledge expression. The expert knowledge base is established to transform the simple logic statements that is easy to be understood by users into complete and complex Clips statements to realize complex fault diagnosis programs， and it is going on to realize the automation and intelligence of real-time fault diagnosis of spacecraft in orbit， accurately locate faults and improve the reliability and safety of satellite systems. Through multi-domain simulation of multiple fault scenarios and project practice， the diagnosis results of the satellite fault diagnosis system are the same as the actual fault data received by the satellite， which verifies the effectiveness of the designed system.

A hybrid optimization method based on deep neural networks is proposed in this paper for the spacecraft pursuit-evasion game problem with free terminal time and {\mathrm{J}}_{\mathrm{2}} perturbation considerations. Firstly，the data set is generated by solving the two-point boundary value problem without {\mathrm{J}}_{\mathrm{2}} perturbation using traditional optimization algorithms. Then， on the basis of that， a deep neural network is established to fit the relationship between the initial state and the solution， and the initial guess solution is generated. Finally， the solution is further optimized by using a local optimization algorithm. Through simulation and verification， it is demonstrated that this method not only performs good feasibility and robustness but also significantly improves computational efficiency， which is compared with traditional hybrid optimization algorithms.