In order to get rid of the dependence on high-precision centrifuges in accelerometer nonlinear coefficients calibration, this paper proposes a system-level calibration method for field condition. Firstly, a 42-dimension Kalman filter is constructed to reduce impact brought by turntable. Then, a biaxial rotation path is designed based on the accelerometer output model, including orthogonal 22 positions and tilt 12 positions, which enhances gravity excitation on nonlinear coefficients of accelerometer. Finally, sampling is carried out for calibration and further experiments. The results of static inertial navigation experiments lasting 4000 s show that compared with the traditional method, the proposed method reduces the position error by about 390 m.

In low Earth orbit (LEO) satellite networks, on-board energy resources of each satellite are extremely limited. And with the increase of the node number and the traffic transmission pressure, the energy consumption in the networks presents uneven distribution. To achieve energy balance in networks, an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor (DEF) is proposed. The DEF is defined as the function of the inter-satellite link distance and the cumulative network energy consumption ratio. According to the minimum sum of DEF on inter-satellite links, an energy consumption balancing algorithm based on DEF is proposed, which can realize dynamic traffic transmission optimization of multiple traffic services. It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network, make full use of idle nodes with low energy consumption, and optimize the energy consumption distribution of the whole network according to the continuous iterations of each traffic service flow. Simulation results show that, compared with the traditional shortest path algorithm, the proposed method can improve the balancing performance of nodes by 75% under certain traffic pressure, and realize the optimization of energy consumption balancing of the whole network.

Developing intelligent unmanned swarm systems (IUSSs) is a highly intricate process. Although current simulators and toolchains have made a notable contribution to the development of algorithms for IUSSs, they tend to concentrate on isolated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner. Furthermore, the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms. Therefore, a comprehensive solution must be developed that encompasses the entire IUSS development lifecycle. In this study, we present the RflySim ToolChain, which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs. The RflySim ToolChain employs a model-based design (MBD) approach, integrating a modeling and simulation module, a lower reliable control module, and an upper swarm decision-making module. This comprehensive integration encompasses the entire process, from modeling and simulation to testing and deployment, thereby enabling users to rapidly construct and validate IUSSs. The principal advantages of the RflySim ToolChain are as follows: it provides a comprehensive solution that meets the full-stack development needs of IUSSs; the highly modular architecture and comprehensive software development kit (SDK) facilitate the automation of the entire IUSS development process. Furthermore, the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment, which is known as the simulation to reality (Sim2Real) process. This paper presents a series of case studies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.

This paper considers the short-range sensing implementation in continuous-wave (CW) phased array systems. We specifically address this CW short-range sensing challenges stemming from the self-interference cancellation (SIC) operation and synthesis requirement of arbitrary beampatterns for the sensing purpose, which has rarely been researched before. In this paper, unlike the only existed work that exploits the heuristic method and shares no analytical solution, an SIC pattern synthesis design is presented with a closed-form solution. By utilizing the null-space projection (NSP) method, the proposed method effectively mitigates the self-interference to enable the in-band full-duplex operation of the array system. Subsequently, the NSP design will be innovatively embedded in a singular value decomposition (SVD) based weighted alternating reserve projection (WARP) approach to efficiently synthesize an arbitrary desired pattern by solving a unique rank-deficient weighted least mean square problem. Numerical results validate the effectiveness of the proposed method in terms of beampattern, SIC performance, and sensing performance.

High complexity and uncertainty of air combat pose significant challenges to target intention prediction. Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns. Accordingly, this study proposes a Mogrifier gate recurrent unit-D (Mog-GRU-D) model to address the combat target intention prediction issue under the incomplete information condition. The proposed model directly processes missing data while reducing the independence between inputs and output states. A total of 1200 samples from twelve continuous moments are captured through the combat simulation system, each of which consists of seven dimensional features. To benchmark the experiment, a missing valued dataset has been generated by randomly removing 20% of the original data. Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25% when dealing with incomplete information. This study provides possible interpretations for the principle of target interactive mechanism, highlighting the model’s effectiveness in potential air warfare implementation.

As commercial drone delivery becomes increasingly popular, the extension of the vehicle routing problem with drones (VRPD) is emerging as an optimization problem of interests. This paper studies a variant of VRPD in multi-trip and multi-drop (VRP-mmD). The problem aims at making schedules for the trucks and drones such that the total travel time is minimized. This paper formulate the problem with a mixed integer programming model and propose a two-phase algorithm, i.e., a parallel route construction heuristic (PRCH) for the first phase and an adaptive neighbor searching heuristic (ANSH) for the second phase. The PRCH generates an initial solution by concurrently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase. Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase. Numerical tests on some benchmark data are conducted to verify the performance of the algorithm. The results show that the proposed algorithm can found better solutions than some state-of-the-art methods for all instances. Moreover, an extensive analysis highlights the stability of the proposed algorithm.

In this paper, we propose an improved YOLOv5-based object detection method for radar images, which have the characteristics of diffuse weak noise and imaging distortion. To mitigate the effects of noise without losing spatial information, an coordinate attention (CA) has been added to pre-extract the feature of the images, which can guarantee a better feature extraction ability. A new stochastic weighted average (SWA) method is designed to refine generalization ability of the algorithm, where the medium mean is used instead of their average value. By introducing an deformable convolution, both regular and irregular images can be proceeded. The experimental results show that the improved algorithm performs better in object detection of radar images compared with the YOLOv5 model, which confirms the effectiveness and feasibility of our model.

The operational readiness test (ORT), like weapon testing before firing, is becoming more and more important for systems used in the field. However, the test requirement of the ORT is distinctive. Specifically, the rule of selecting test items should be changed in different test turns, and whether there is a fault is more important than where the fault is. The popular dependency matrix (D-matrix) processing algorithms becomes low efficient because they cannot change their optimizing direction and spend unnecessary time on fault localization and isolation. To this end, this paper proposes a D-matrix processing algorithm named piecewise heuristic algorithm for D-matrix (PHAD). Its key idea is to use a piecewise function comprised of multiple different functions instead of the commonly used fixed function and switch subfunctions according to the test stage. In this manner, PHAD has the capability of changing optimizing direction, precisely matching the variant test requirements, and generating an efficient test sequence. The experiments on the random matrixes of different sizes and densities prove that the proposed algorithm performs better than the classical algorithms in terms of expected test cost (ETC) and other metrics. More generally, the piecewise heuristic function shows a new way to design D-matrix processing algorithm and a more flexible heuristic function to meet more complicated test requirements.

Project construction and development are an important part of future army designs. In today’s world, intelligent warfare and joint operations have become the dominant developments in warfare, so the construction and development of the army need top-down, top-level design, and comprehensive planning. The traditional project development model is no longer sufficient to meet the army’s complex capability requirements. Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effectiveness. At the same time, when a program consists of large-scale project data, the effectiveness of the traditional, precise mathematical planning method is greatly reduced because it is time-consuming, costly, and impractical. To solve above problems, this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algorithm and verifies the effectiveness and feasibility of the model and algorithm through an example. The results show that the hybrid algorithm proposed in this paper is better than the existing meta-heuristic algorithm.

To enhance direction of arrival (DOA) estimation accuracy, this paper proposes a low-cost method for calibrating far-field steering vectors of large aperture millimeter wave radar (mmWR). To this end, we first derive the steering vectors with amplitude and phase errors, assuming that mmWR works in the time-sharing mode. Then, approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed, which is used to accomplish the mapping between the two of them. Finally, simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.

This paper presents a method of multicopter interception control based on visual servo and virtual tube in a cluttered environment. The proposed hybrid heuristic function improves the efficiency of the A* algorithm. The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A* algorithm. In six different simulation scenarios, the efficiency of the modified A* algorithm is 6.2% higher than that of the traditional A* algorithm. The efficiency of path planning and virtual tube planning is verified by simulations. The effectiveness of interception control is verified by a software-in-loop (SIL) simulation.

This paper proposes a reliability evaluation model for a multi-dimensional network system, which has potential to be applied to the internet of things or other practical networks. A multi-dimensional network system with one source element and multiple sink elements is considered first. Each element can connect with other elements within a stochastic connection ranges. The system is regarded as successful as long as the source element remains connected with all sink elements. An importance measure is proposed to evaluate the performance of non-source elements. Furthermore, to calculate the system reliability and the element importance measure, a multi-valued decision diagram based approach is structured and its complexity is analyzed. Finally, a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the element importance measure.

The multifunctional integration system (MFIS) is based on a common hardware platform that controls and regulates the system’s configurable parameters through software to meet different operational requirements. Dwell scheduling is a key for the system to realize multifunction and maximize the resource utilization. In this paper, an adaptive dwell scheduling optimization model for MFIS which considers the aperture partition and joint radar communication (JRC) waveform is established. To solve the formulated optimization problem, JRC scheduling conditions are proposed, including time overlapping condition, beam direction condition and aperture condition. Meanwhile, an effective mechanism to dynamically occupy and release the aperture resource is introduced, where the time-pointer will slide to the earliest ending time of all currently scheduled tasks so that the occupied aperture resource can be released timely. Based on them, an adaptive dwell scheduling algorithm for MFIS with aperture partition and JRC waveform is put forward. Simulation results demonstrate that the proposed algorithm has better comprehensive scheduling performance than up-to-date algorithms in all considered metrics.

The dwell scheduling problem for a multifunctional radar system is led to the formation of corresponding optimization problem. In order to solve the resulting optimization problem, the dwell scheduling process in a scheduling interval (SI) is formulated as a Markov decision process (MDP), where the state, action, and reward are specified for this dwell scheduling problem. Specially, the action is defined as scheduling the task on the left side, right side or in the middle of the radar idle timeline, which reduces the action space effectively and accelerates the convergence of the training. Through the above process, a model-free reinforcement learning framework is established. Then, an adaptive dwell scheduling method based on Q-learning is proposed, where the converged Q value table after training is utilized to instruct the scheduling process. Simulation results demonstrate that compared with existing dwell scheduling algorithms, the proposed one can achieve better scheduling performance considering the urgency criterion, the importance criterion and the desired execution time criterion comprehensively. The average running time shows the proposed algorithm has real-time performance.

Unmanned aerial vehicles (UAVs) have become one of the key technologies to achieve future data collection due to their high mobility, rapid deployment, low cost, and the ability to establish line-of-sight communication links. However, when UAV swarm perform tasks in narrow spaces, they often encounter various spatial obstacles, building shielding materials, and high-speed node movements, which result in intermittent network communication links and cannot support the smooth completion of tasks. In this paper, a high mobility and dynamic topology of the UAV swarm is particularly considered and the high dynamic mobile topology-based clustering (HDMTC) algorithm is proposed. Simulation and real flight verification results verify that the proposed HDMTC algorithm achieves higher stability of network, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for UAV swarm networks.

How multi-unmanned aerial vehicles (UAVs) carrying a payload pass an obstacle-dense environment is practically important. Up to now, there have been few results on safe motion planning for the multi-UAVs cooperative transportation system (CTS) to pass through such an environment. The problem is challenging because it is difficult to analyze and explicitly take into account the swing motion of the payload in planning. In this paper, a modeling method of virtual tube is proposed by fusing the advantages of the existing modeling algorithm for regular virtual tube and the expansion environment method. The proposed method can not only generate a safe and smooth tube for UAVs, but also ensure the payload stays away from the dense obstacles. Simulation results show the effectiveness of the method and the safety of the planned tube.

In wideband noncooperative interference cancellation, the reference signals obtained through auxiliary antennas are weighted to cancel with the interference signal. The correlation between the reference signal and the interference signal determines interference cancellation performance, while the auxiliary antenna array affects the correlation by influencing the amplitude and phase of the reference signals. This paper analyzes the effect of auxiliary antenna array on multiple performances of wideband noncooperative interference cancellation. Firstly, the array received signal model of wideband interference is established, and the weight vector coupled with the auxiliary antennas array manifold is solved by spectral analysis and eigen-subspace decomposition. Then, multiple performances which include cancellation resolution, grating null, wideband interference cancellation ratio (ICR), and convergence rate are quantitatively characterized with the auxiliary antenna array. It is obtained through analysis that the performances mutually restrict the auxiliary antenna array. Higher cancellation resolution requires larger array aperture, but when the number of auxiliary antennas is fixed, larger array aperture results in more grating nulls. When the auxiliary antennas are closer to the main antenna, the wideband ICR is improved, but the convergence rate is reduced. The conclusions are verified through simulation of one-dimensional uniform array and two-dimensional nonuniform array. The experiments of three arrays are compared, and the results conform well with simulation and support the theoretical analysis.

To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition. This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat. This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment. Simulation results demonstrate that, compared to classical intention recognition models, the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.

This paper presents a quadcopter system for navigation in outdoor urban environments. The main contributions include the hardware design, the establishment of global occupancy grid maps based on millimeter-wave radars, the trajectory planning scheme based on optimal virtual tube methods, and the controller structure based on dynamics. The proposed system focuses on utilizing a compact and lightweight quadrotor with sensors to achieve navigation that conforms to the direction of urban roads with high computational efficiency and safety. Our work is an application of millimeter-wave radars and virtual tube planning for obstacle avoidance in navigation. The validness and effectiveness of the proposed system are verified by experiments.

According to the measurement principle of the traditional interferometer, a narrowband signal model is established and used, however, for wideband signals or multiple signals, this model is invalid. For the problems of direction finding with interferometer for wideband signals and multiple signals scene, a frequency domain phase interferometer is proposed and the concrete implementation scheme is given. The proposed method computes the phase difference in frequency domain, and finds multi-target results with judging the spectrum amplitude changing, and uses the frequency phase difference to compute the arrival angle. Theoretical analysis and simulation results show that the proposed method effectively solves the problem of the angle estimation with phase interferometer for wideband signals, and has good performance in multiple signals scene with non-overlapping spectrum or partially overlapping. In addition, the wider the signal bandwidth, the better direction finding performance of this algorithm.

Complex systems widely exist in nature and human society. There are complex interactions between system elements in a complex system, and systems show complex features at the macro level, such as emergence, self-organization, uncertainty, and dynamics. These complex features make it difficult to understand the internal operation mechanism of complex systems. Networked modeling of complex systems is a favorable means of understanding complex systems. It not only represents complex interactions but also reflects essential attributes of complex systems. This paper summarizes the research progress of complex systems modeling and analysis from the perspective of network science, including networked modeling, vital node analysis, network invulnerability analysis, network disintegration analysis, resilience analysis, complex network link prediction, and the attacker-defender game in complex networks. In addition, this paper presents some points of view on the trend and focus of future research on network analysis of complex systems.

In this paper, a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented, focusing on the challenges posed by sea clutter and multipath scattering. The performance of the radar detection methods under sea clutter, multipath, and combined conditions is categorized and summarized, and future research directions are outlined to enhance radar detection performance for low–altitude targets in maritime environments.

A three-dimensional path-planning approach has been developed to coordinate multiple fixed-wing unmanned aerial vehicles (UAVs) while avoiding collisions. The hierarchical path-planning architecture that divides the path-planning process into two layers is proposed by designing the velocity-obstacle strategy for satisfying timeliness and effectiveness. The upper-level layer focuses on creating an efficient Dubins initial path considering the dynamic constraints of the fixed wing. Subsequently, the lower-level layer detects potential collisions and adjusts its flight paths to avoid collisions by using the three-dimensional velocity obstacle method, which describes the maneuvering space of collision avoidance as the intersection space of half space. To further handle the dynamic and collision-avoidance constraints, a priority mechanism is designed to ensure that the adjusted path is still feasible for fixed-wing UAVs. Simulation experiments demonstrate the effectiveness of the proposed method.

In this paper, the newly-derived maximum correntropy Kalman filter (MCKF) is re-derived from the M-estimation perspective, where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions. Based on the derivation process, a unified form for the robust Gaussian filters (RGF) based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement. The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals. Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust anti-jamming performance.

Long-time coherent integration (LTCI) is an effective way for radar maneuvering target detection, but it faces the problem of a large number of search parameters and large amount of calculation. Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter background, and at the same time improving the calculation efficiency has become an urgent problem to be solved. The sparse transformation theory is introduced to LTCI in this paper, and a non-parametric searching sparse LTCI (SLTCI) based maneuvering target detection method is proposed. This method performs time reversal (TR) and second-order Keystone transform (SKT) in the range frequency & slow-time data to complete high-order range walk compensation, and achieves the coherent integration of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform (RSFRFT). It can compensate for the nonlinear range migration caused by high-order motion. S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method, which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.

This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods. The purpose of the spacecraft is to inspect the entire surface of a non-cooperative target with active maneuverability in front lighting. First, the impulsive orbital game problem is formulated as a turn-based sequential game problem. Second, several typical relative orbit transfers are encapsulated into modules to construct a parameterized action space containing discrete modules and continuous parameters, and multi-pass deep Q-networks (MPDQN) algorithm is used to implement autonomous decision-making. Then, a curriculum learning method is used to gradually increase the difficulty of the training scenario. The backtracking proportional self-play training framework is used to enhance the agent’s ability to defeat inconsistent strategies by building a pool of opponents. The behavior variations of the agents during training indicate that the intelligent game system gradually evolves towards an equilibrium situation. The restraint relations between the agents show that the agents steadily improve the strategy. The influence of various factors on game results is tested.

As high-dynamics and weak-signal are of two primary concerns of navigation using Global Navigation Satellite System (GNSS) signals, an acquisition algorithm based on three-time fractional Fourier transform (FRFT) is presented to simplify the calculation effectively. Firstly, the correlation results similar to linear frequency modulated (LFM) signals are derived on the basis of the high dynamic GNSS signal model. Then, the principle of obtaining the optimum rotation angle is analyzed, which is measured by FRFT projection lengths with two selected rotation angles. Finally, Doppler shift, Doppler rate, and code phase are accurately estimated in a real-time and low signal to noise ratio (SNR) wireless communication system. The theoretical analysis and simulation results show that the fast FRFT algorithm can accurately estimate the high dynamic parameters by converting the traditional two-dimensional search process to only three times FRFT. While the acquisition performance is basically the same, the computational complexity and running time are greatly reduced, which is more conductive to practical application.

In this paper, we study the orthogonal time frequency space signal transmission over multi-path channel in the presence of phase noise (PHN) at both sides of millimeter wave (mmWave) communication links. The statistics characteristics of the PHN-induced common phase error and inter-Doppler interference are investigated. Then, a column-shaped pilot structure is designed, and training pilots are used to realize linear-complexity PHN tracking and compensation. Numerical results demonstrate that the proposed scheme enables the signal to noise ratio loss to be restrained within 1 dB in contrast to the no PHN case.

Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks. Nevertheless, the fog computing Internet-of-Things (IoT) systems are susceptible to malicious eavesdropping attacks during the information transmission, and this issue has not been adequately addressed. In this paper, we propose a physical-layer secure fog computing IoT system model, which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers. The secrecy rate of the proposed model is analyzed, and the quantum galaxy–based search algorithm (QGSA) is proposed to solve the hybrid task scheduling and resource management problem of the network. The computational complexity and convergence of the proposed algorithm are analyzed. Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks. Moreover, the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.

The emergence of laser technology has led to the gradual integration of laser weapon system (LaWS) into military scene, particularly in the field of anti-unmanned aerial vehicle (UAV), showcasing significant potential. However, A current limitation lies in the absence of a comprehensive quantitative approach to assess the capabilities of LaWS. To address this issue, a damage effectiveness characterization model for LaWS is established, taking into account the properties of laser transmission through the atmosphere and the thermal damage effects. By employing this model, key parameters pertaining to the effectiveness of laser damage are determined. The impact of various spatial positions and atmospheric conditions on the damage effectiveness of LaWS have been examined, employing simulation experiments with diverse parameters. The conclusions indicate that the damage effectiveness of LaWS is contingent upon the spatial position of the target, resulting in a diminished effectiveness to damage on distant, low-altitude targets. Additionally, the damage effectiveness of LaWS is heavily reliant on the atmospheric condition, particularly in complex settings such as midday and low visibility conditions, where the damage effectiveness is substantially reduced. This paper provides an accurate and effective calculation method for the rapid decision-making of the operators.

In the realm of missile defense systems, the self-sufficient maneuver capacity of missile swarms is pivotal for their survival. Through the analysis of the missile dynamics model, a time-efficient cooperative attack strategy for missile swarm is proposed. Based on the distribution of the attackers and defenders, the collision avoidance against the defenders is considered during the attack process. By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders, the collision avoidance constrains of attacking swarm are redefined. The key point is on adjusting the relative velocity vectors to fall outside the collision cone. This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process. By leveraging an innovative repulsion artificial function, a time-efficient cooperative attack strategy for missile swarm is obtained. Through rigorous simulation, the effectiveness of this cooperative attack strategy is substantiated. Furthermore, by employing Monte Carlo simulation, the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.

Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.

In this paper, an online midcourse guidance method for intercepting high-speed maneuvering targets is proposed. Firstly, the affine system is used to build a dynamic model and analyze the state constraints. The midcourse guidance problem is transformed into a continuous time optimization problem. Secondly, the problem is transformed into a discrete convex programming problem by affine control variable relaxation, Gaussian pseudospectral discretization and constraints linearization. Then, the off-line midcourse guidance trajectory is generated before midcourse guidance. It is used as the initial reference trajectory for online correction of midcourse guidance. An online guidance framework is used to eliminate the error caused by calculation of guidance instruction time. And the design of discrete points decreases with flight time to improve the solving efficiency. In addition, it is proposed that the terminal guidance capture is used innovatively space to judge the success of midcourse guidance. Numerical simulation shows the feasibility and effectiveness of the proposed method.