The Maneuvering flight strategy based on the guidance commands is an effective approach to keep the escape flight vehicle (EFV) away from the pursuit flight vehicle (PFV). In this paper, we consider the scenario where the EFV utilizes a dynamic programming method for generating guidance commands, while the PFV employs the proportional navigation method. Our objective is to progressively maximize the evasion distance, defined as the minimum distance between the EFV and the PFV during the battle process. For solving this challenging max-min problem, a two-step strategy is conceived. In the first step, within a reducedsize solution space, we employ the brute-force search method to identify four sets of locally optimal solutions. In the second step, we dynamically determine the reasonable range of the discrete guidance commands at each time instant, and execute the optimal guidance commands with the largest predictive evasion distance, which is ascertained in a virtual scenario. The results indicate that the evasion distance can be continuously improved by using the proposed guidance design method, and the evasion distance has positive correlation with the number of the virtual calculation step.

In a distributed radar system, a signal fusion-based target detection algorithm can yield better detection performance compared to traditional decision fusion-based algorithm, thus has drawn considerable attention. In practice, however, signal fusion-based detection algorithm for distributed radar encounters two primary challenges: one is excessive communication cost and the other is difficult to fuse locally unregistered signals which is usually ignored by existing researches. To resolve these challenges, this paper presents a two-step signal registration algorithm with locally censored observations. Specifically, only local signals exceeding an initial threshold are sent to a fusion center for signal registration. At the first step of this algorithm, Mahalanobis Distance is employed as a metric to identify physically admissible observations, referred to as candidate observations. At the second step, the maximum likelihood estimation criterion is applied to eliminate potential false alarms among these candidate observations, thus determining the final set of registered observations for signal fusion. Numerical results indicate that the algorithm presented herein is capable of efficiently tackling the signal registration problem as well as alleviating communication burden. Furthermore, it achieves nearly lossless detection performance in comparison with its unregistered counterpart. This algorithm can be applied to practical engineering applications, since the simulation parameters are aligned with real radar scenarios.

Guidance commands of flight vehicles can be regarded as a series of data sets having fixed time intervals; thus, guidance design constitutes a typical sequential decision problem and satisfies the basic conditions for using the deep reinforcement learning (DRL) technique. In this paper, we consider the scenario where the escape flight vehicle (EFV) generates guidance commands based on the DRL technique, while the pursuit flight vehicles (PFVs) derive their guidance commands employing the proportional navigation method. For every PFV, the evasion distance is described as the minimum distance between the EFV and the PFV during the escape-and-pursuit process. For the EFV, the objective of the guidance design entails progressively maximizing the residual velocity, which is described as the EFV’s velocity when the last evasion distance is attained, subject to the constraint imposed by the given evasion distance threshold. In the outlined problem, three dimensionalities of uncertainty emerge: (1) the number of PFVs requiring evasion at each time instant; (2) the precise time instant at which each of the evasion distances can be attained; (3) whether each attained evasion distance exceeds the given threshold or not. To solve the challenging problem, we propose an innovative solution that integrates the recurrent neural network (RNN) with the proximal policy optimization (PPO) algorithm, engineered to generate the guidance commands of the EFV. Initially, the model, trained by the RNN-based PPO algorithm, demonstrates effectiveness in evading a single PFV. Subsequently, the aforementioned model is deployed to evade additional PFVs, thereby systematically augmenting the model’s capabilities. Comprehensive simulation outcomes substantiate that the guidance design method based on the proposed RNN-based PPO algorithm is highly effective.

In distributed radar, a local radar site may perform coherent accumulation for a better signal-to-noise (SNR) output and it is often unnecessary to transmit statistics in all Doppler channels to a fusion center (FC), if the probability that more than one target is present in a range cell is low. In this paper, we propose a low-communication-cost algorithm for distributed detection with locally coherent multi-pulse accumulation. Under the assumption that no more than one target is present in a range cell, we take the maximum of the test statistics over all Doppler channels to represent the presence of a target and then quantize the statistic maximum with the least squares quantization (LSQ) algorithm for a low communication bandwidth requirement. An optimal fusion rule based on the Neyman-Pearson (NP) criterion is formulated to make a global decision under a constant false alarm rate. Numerical results indicate that compared with traditional algorithm, the proposed algorithm with 3-bit LSQ quantization results in less than 0.5 dB SNR loss whereas the communication bandwidth is reduced by 99.71%.

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy.

Multi-source perception fusion positioning technology based on heterogeneous sensors is a key research area in the field of target positioning. The heterogeneous sensor positioning system composed of radar and infrared has a wide range of applications. However, existing works mostly use radar measurement to expand the dimension of infrared data to locate target, which will introduce the large angle measurement error of radar into infrared. In this paper, we propose a radar/infrared linear weighted least-squares (WLS) localization algorithm based on target motion model. First, we build linear equations of target position relative to radar and infrared based on the space relationship, respectively. In this manner, the position of target can be solved through a linear optimization problem. Two kinds of equations are combined by deformation and solved by WLS algorithm to achieve the target localization. Simulation results show that the proposed method has better target localization performance than that of single radar, and with the improvement of the measurement accuracy of radar/infrared sensors, the localization effect is better.

Guidance commands of flight vehicles can be regarded as a series of data sets having fixed time intervals, thus guidance design constitutes a typical sequential decision problem and satisfies the basic conditions for using the deep reinforcement learning (DRL) technique. In this paper, we consider the scenario where the escape flight vehicle (EFV) generates guidance commands based on the DRL technique and the pursuit flight vehicle (PFV) generates guidance commands based on the proportional navigation method. Evasion distance is described as the minimum distance between the EFV and the PFV during the escape-and-pursuit process. For the EFV, the objective of the guidance design entails progressively maximizing the residual velocity, which is described as the EFV's velocity when the evasion distance occurs, subject to the constraint imposed by the given evasion distance. Thus an irregular dynamic max-min problem of extremely large-scale is formulated. In this problem, the time instant when the optimal solution (i.e., the maximum residual velocity satisfying the evasion distance constraint) can be attained is uncertain and the optimum solution is dependent on all the intermediate guidance commands generated before. For solving this challenging problem, a two-step strategy is conceived. In the first step, we use the proximal policy optimization (PPO) algorithm to generate the guidance commands of the EFV. The results obtained by PPO in the global search space are coarse, despite the fact that the reward function, the neural network parameters and the learning rate are designed elaborately. Therefore, in the second step, we propose to invoke the evolution strategy (ES) based algorithm, which uses the result of PPO as the initial value, to further improve the quality of the solution by searching in the local space. Extensive simulation results demonstrate that the proposed guidance design method based on the PPO algorithm is capable of achieving a residual velocity of 67.24 m/s, higher than the residual velocities achieved by the benchmark soft actor-critic and deep deterministic policy gradient algorithms. Furthermore, the proposed ES-enhanced PPO algorithm outperforms the PPO algorithm by 2.7%, achieving a residual velocity of 69.04 m/s.

Shortening the ground preparation time of vehicular spacecraft is an important topic in the aerospace field. The time is hard to be significantly shortened using the traditional methods because initial alignment and spacecraft erection must be fulfilled in order. To solve the problem, an estimation and correction method for azimuth and attitude errors of attitude tracking is proposed in this article. We propose a real-time error estimation model to reveal the effect of the initial azimuth, attitude errors, and the gyro biases of the spacecraft inertial measurement unit (SIMU) on the current azimuth and attitude errors during attitude tracking, making it possible to post-compensate the initial coarse azimuth error and the gyro biases of the SIMU at the end of attitude tracking. This model is the key and the theoretical basis of our proposed method. Detailed estimation and correction steps are designed, to achieve the purpose of fulfilling the spacecraft erection and the fine-alignment of the vehicular inertial measurement unit (VIMU) in parallel. The results of the simulations and experiments show that the preparation time is remarkably shortened using the proposed method, without decreasing the final azimuth and attitude accuracy of the SIMU. This proposed method detaches the inherent viewpoint that fine-alignment must be completed before attitude tracking and spacecraft erection in the traditional horizontal collimation scheme. It provides a completely new way to shorten the ground preparation time of vehicular spacecraft.

航天批生产精益产品保证,是针对航天产品和批生产任务的特点,面向批生产全过程,充分聚焦重点管控项目,突出关注航天核心产品,强化关键生产环节管控,全面、有序、正向、规范地实施批生产过程的质量管控项目,并通过优化和改进,持续提升批次产品的合格率、稳定性及一次发射飞行的成功率.

This article presents a numerical study to investigate aerodynamic heating induced by lateral jet interaction of a four-stage micro rocket in subsonic, supersonic and hypersonic regimes, with particular emphasis on the effects of trajectory altitude, angle of attack, and boundary layer flow pattern. A three-dimensional compressible RANS solver is used for the study. To describe the effects of high temperature, a thermally perfect gas is assumed. The analysis shows that remarkable aerodynamic heating is generated at an immediate upstream of the nozzle exit in supersonic/hypersonic regime. In addition, the interaction between two lateral jets is noteworthy and the maximum interference factor of heat flux reach up to 100 near the central meridian region at high altitudes. Besides, the peak heating around the nozzles almost decrease linearly with increasing angle of attack. The study also shows that there is no big difference of peak heating magnitude between the laminar and turbulence and sometimes the laminar case even has a larger peak heating. Conscious attention should be paid to this phenomenon and retain the safety of vehicle.

A laminated silicon nitride (Si3N4) ceramic material with a hollow, oriented, one-dimensional microstructure was successfully prepared based on the tape casting and sacrificial template method. The results show that hollow, oriented, one-dimensional microstructures can effectively induce crack deflection. Different arrangements of the structural design layer and dense layer will have different effects on the material. In particular, bulks with a single-layer orthogonal arrangement of the structural design layer possess high toughness and obvious crack deflection during the fracture process. A kind of multiscale crack deflection mode was realized. Compared with the fracture toughness of the monolithic Si3N4 ceramic bulk (5.55 MPa m1/2), the fracture toughness can reach 8.73 MPa m1/2, and the flexural strength can still reach 391.47 MPa with only a slight decrease.

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem. It is fundamental for efficient information fusion, control and decision in distributed systems. Previously, distributed data synchronization was mainly studied in the latency-tolerant distributed databases, or assuming the general model of wireless ad hoc networks. In this paper, we propose a pair of linear network coding (NC) and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator's control. We consider both data block selection and transmitting node selection for exploiting the benefits of NC. Instead of using the store-and-forward protocol as in the conventional uncoded approach, a compute-and-forward protocol is used in our scheme, which improves the transmission efficiency. The performance of the proposed algorithms is studied under different values of network size, network connection degree, and per-hop packet error rate. Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.

A novel monolithic yttria-alumina aerogel was successfully prepared using inorganic precursors through the solgel method. The yttria-alumina aerogel possesses a three-dimensional network structure composed of numerous nanoscale flake like particles and nanoscale pores. In addition, the aerogel is mainly made up of nano Y2O3 grains, and Al atoms are dissolved in the Y2O3 grains to form the solid solution. The aerogel with a low density of 0.203 g/cm3 exhibits low thermal conductivity of 0.016 W/(m center dot K). Therefore, the yttria-alumina aerogel shows promise for application as a thermal insulation material.

As a special type of mobile ad hoc network (MANET), the flying ad hoc network (FANET) has the potential to enable a variety of emerging applications in both civilian wireless communications (e.g., 5G and 6G) and the defense industry. The routing protocol plays a pivotal role in FANET. However, when designing the routing protocol for FANET, it is conventionally assumed that the aerial nodes move randomly. This is clearly inappropriate for a mission-oriented FANET (MO-FANET), in which the aerial nodes typically move toward a given destination from given departure point(s), possibly along a roughly deterministic flight path while maintaining a well-established formation, in order to carry out certain missions. In this paper, a novel cyber-physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration, which makes full use of the mission-determined trajectory dynamics to construct the time sequence of rejoining and separating, as well as the adjacency matrix for each node, as prior information. Compared with the existing representative routing protocols used in FANETs, our protocol achieves a higher packet-delivery ratio (PDR) at the cost of even lower overhead and lower average end-to-end latency, while maintaining a reasonably moderate and stable network jitter, as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.

基于深度卷积神经网络设计了"智能-校正"算法以快速生成制导指令;深度学习模型是由卷积层、全连接层、批量归一化层和残差网络(ResNet)组合成的一个19层深度神经网络,飞行器状态变量转化为类似于图像像素矩阵的二维矩阵,通过sgd优化和权重衰减正则项将神经网络的训练误差降低至约0.025°;之后针对飞行器运动存在制导指令泛化误差累积的问题又设计了校正环节;最后经多组仿真证明了"智能-校正"算法兼顾了速度和精度,不仅成功实现了构型保持,而且将解算速度提升了约6倍,同时解决了泛化误差累积带来的影响.

In this paper, an improved closed loop guidance method for solid ballistic missile is presented. Firstly, the dynamics model of the rocket is established in the launch inertial coordinate system. Then the conversion relation between the launch coordinate system and the launch inertial coordinate system is illustrated. Secondly, based on the traditional closed loop guidance scheme, an improved closed loop guidance method with the ballistic inclination constraint of the reentry point is presented, which by introducing the reentry point velocity information. Then, to achieve the management of the remaining energy of the solid rocket, a curve planning based energy management scheme is adopted. Combined with the proposed closed loop guidance scheme, the guidance commands which satisfying the complex constraint can be generated in real time. Finally, multiple scenarios under nominal and disturbance conditions are simulated to verify the proposed closed loop guidance with energy management scheme. Results indicate that the proposed method has good attack accuracy and task adaptability.

To deal with the coupling between the attitude and aerodynamic parameters of the hyper-glide vehicle, the dynamic model of attitude/aerodynamic parameter coupling is established based on the idea of state/parameter dimensionality expansion. For the proposed extended dimensional system, a sliding mode observer is constructed to estimate the aerodynamic coupling information and the external disturbance, and a sliding mode controller is further presented. The closed-loop stability of the sliding mode control system based on the sliding mode observer is proved, and the calculation methods of the control parameters and the observer parameters are given. Simulation results indicate that the extended dimensional coupling model can characterize the mutual coupling relationship between flight attitude and aerodynamic parameters; the proposed sliding mode control system can reduce the effects of external compound disturbances effectively and improve the dynamic and static characteristics.

针对卫星舱壳体分离过程中柔性效应带来的影响,基于柔性多体动力学方法,建立了复合材料卫星舱壳体平抛式分离的刚柔耦合动力学模型,采用推冲器的推力-位移关系实现了从地面试验到任意工况的推冲器推力等效,对柔性体模型和刚体模型进行了仿真及对比分析.结果 表明:壳片弹性变形以2阶“呼吸运动”模态为主,吸收了约推冲器做功的15％,分离速度降低;分离面下角点处“呼吸运动”最为剧烈,半罩第一次向内“呼吸运动”时对内包络的影响最大;柔性效应还会影响推冲器推力作用的时间历程.进行卫星舱分离地面试验,采用高速摄影测量半罩运动情况,对比验证了仿真方法的正确性,仿真误差在13％以内.研究结果有助于确保分离方案的有效性,释放了卫星舱内包络空间的设计余量,为分离优化设计提供参考.

To address the problem of keeping the formation for multiple vehicles, this paper proposes a novel algorithm based on non-dominated sorting genetic algorithm II (NSGA- II) and deep convolutional neural network(DCNN). Firstly, the problem of keeping the formation for multiple vehicles is translated into three objective functions. Secondly, NSGA- II is used to optimize and a strategy is designed to choose the guidance command from Pareto solution set. Thirdly, in order to improve the calculation speed, a 19-layer deep learning model is built based on DCNN, residual network and batch normalization layer, then a correction part is designed to avoid the phenomenon that the accumulation of generalization error will brings obvious error on keeping the formation. Finally, three sets of simulation demonstrate the effectiveness of this algorithm.

考虑分离安全性的运载火箭卫星舱壳体结构设计,基于柔性多体动力学理论,建立了卫星舱壳体平抛分离参数化模型,采用基于方差测度的非线性回归法,对卫星舱壳体各分区结构尺寸、铺层厚度、推冲器作用位置等参数进行灵敏度分析.选取重要设计变量,以壳体质量最轻为优化目标,以分离速度和分离最小间隙为约束,应用近似模型进行优化设计.优化后的卫星舱壳体质量降低48.3％,提高了火箭的有效载荷运载能力.