Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

Multi-state autonomous drilling for lunar exploration

Due to the lack of information of subsurface lunar regolith stratification which varies along depth, the drilling device may encounter lunar soil and lunar rock randomly in the drilling process. To meet the load safety requirements of unmanned sampling mission under limited orbital resources, the control strategy of autonomous drilling should adapt to the indeterminable lunar environments. Based on the analysis of two types of typical drilling media (i.e., lunar soil and lunar rock), this paper proposes a multi-state control strategy for autonomous lunar drilling. To represent the working circumstances in the lunar subsurface and reduce the complexity of the control algo-rithm, lunar drilling process was categorized into three drilling states:the interface detection, initi-ation of drilling parameters for recognition and drilling medium recognition. Support vector machine (SVM) and continuous wavelet transform were employed for the online recognition of dril-ling media and interface, respectively. Finite state machine was utilized to control the transition among different drilling states. To verify the effectiveness of the multi-state control strategy, drilling experiments were implemented with multi-layered drilling media constructed by lunar soil simulant and lunar rock simulant. The results reveal that the multi-state control method is capable of detect-ing drilling state variation and adjusting drilling parameters timely under vibration interferences. The multi-state control method provides a feasible reference for the control of extraterrestrial autonomous drilling.     

Effects of process parameters on mechanical properties of abrasive-assisted electroformed nickel

A cathode mandrel with translational and rotational motion, which was supposed to obtain uniform friction effect on surface, was employed in abrasive-assisted electroforming for revolving parts with complex profile. The effects of current density, translational speed and rotational speed on the deposit properties were studied by orthogonal test. The tensile strength, elongation and micro hardness value were measured to find out how the factors affected the properties. The optimized results show that changes of current density affect the tensile strength of nickel layer most, while translational speed has the most remarkable influences on both elonga-tion and micro hardness. The low rotational speed affects the properties least. In this experiment, a smooth nickel layer with tensile strength 581 MPa, elongation 17%and micro hardness 248HV is obtained by the orthogonal test.     

低速大转矩永磁游标电机在发酵罐中的应用

采用秸秆生产丁醇时,为了达到高效率发酵,需要使发酵液中菌体分布均匀,所加物料需要及时搅拌。工业化生产丁醇的发酵罐尺寸较大,搅拌过程对驱动电机的输出转矩要求较高,因此高效率、小体积、低速大转矩的秸秆发酵搅拌电机对工业化发酵生产丁醇意义重大。设计了一种定转子永磁游标电机用于发酵液搅拌,并对电机的槽极数、电磁性能进行了具体的研究。制造样机并采用滞环控制实现了对液态发酵对象的快速响应,且实现了低能耗、高效率的发酵过程。     

磁强计数据辨识卫星自旋状态

针对近地低轨三轴稳定卫星在轨管理后期,除磁强计外其他姿态敏感器都无效情况下的姿态异常问题,分析了三轴稳定卫星失去姿态基准后,星体自旋状态下三轴磁强计测量数据的特点,提出了使用磁强计测量数据的矢量信息,以找到能够获取卫星状态的方法,从而建立了磁强计测量矢量与卫星自旋轴的几何关系,给出了处于自旋状态下的卫星自旋轴确定方法。通过此种辨识方法,获得了某气象卫星姿态异常翻转状态下的自旋矢量方向和自旋角速度,从而证明了该辨识方法快速、有效,可以作为姿态异常卫星自旋状态的辨识手段,为恢复卫星姿态提供了重要信息,具有一定的工程应用价值。     

A novel biased proportional navigation guidance law for close approach phase

A novel biased proportional navigation guidance (BPNG) law is proposed for the close approach phase, which aims to make the spacecraft rendezvous with the target in specific relative range and direction. Firstly, in order to describe the special guidance requirements, the concept of zero effort miss vector is proposed and the dangerous area where there exists collision risk for safety consideration is defined. Secondly, the BPNG, which decouples the range control and direc-tion control, is designed in the line-of-sight (LOS) rotation coordinate system. The theoretical anal-ysis proves that BPNG meets guidance requirements quite well. Thirdly, for the consideration of fuel consumption, the optimal biased proportional navigation guidance (OBPNG) law is derived by solving the Schwartz inequality. Finally, simulation results show that BPNG is effective for the close approach with the ability of evading the dangerous area and OBPNG consumes less fuel compared with BPNG.     

一种改进的Lucas—Kanade光流估计方法

Lucas—Kanade算法是一种重要的光流估计技术,在计算机视觉领域有广泛的应用。但对于图像纹理欠丰富的区域,Lucas—Kanade算法光流估计结果较差。文章提出了一种改进的Lucas—Kanade算法：首先,计算跟踪置信度因子;然后,通过阈值检测剔除不可靠的光流估计结果;最后,进行邻域填充。该方法改善了纹理欠丰富区域的光流估计,对矮人模型图像序列进行了实验,证明了方法的有效性及优于Poelman方法和Chiba方法。     

风洞试验绳牵引冗余并联机器人的刚度增强与运动控制

 以一种应用于低速风洞试验的6自由度绳牵引冗余并联机器人为研究对象,解决飞行器模型在风荷作用下动态定位误差大、力学测量精度不高的问题。运用微分变换推导了该机器人的刚度解析表达式;分别建立了末端执行器和驱动系统的动力学模型;针对系统冗余驱动、强耦合和非线性的特点,设计了基于刚度增强准则的关节空间比例微分（PD）修正前馈控制器,实现了以提高飞行器升力方向、顺风向和俯仰转动方向刚度为目标的绳系张力优化分配。数值仿真算例表明,与基于张力极小准则的控制器相比,前者有效地提高了飞行器的动态定位精度。     

基于在线LS-SVM算法的变参数混沌时间序列预测

研究利用最小二乘支持向量机(LS-SVM)预测变参数混沌时间序列。变参数混沌系统适合于描述现实中的复杂混沌现象,但由于参数的慢变导致系统动力学特性不断发生变化,基于Tan-kens嵌入定理的建模预测方法难以适用,其时间序列预测可以看作是小样本学习问题。最小二乘支持向量机是在二次损失函数下采用等式约束求解问题的一种支持向量机,保留支持向量机优点同时计算量大大减少。提出用一种具有遗忘机制的最小二乘支持向量机在线递推算法,并引入历史数据的高次项预测变参数混沌时间序列。对典型变参数混沌时间序列的预测结果表明,该方法具有较高预测精度,能快速跟踪预测变参数混沌时间序列。