小型寻的拦截器和它的轨控微型固体火箭综述

较详细的介绍了美国小型寻的反卫星拦截器和它的轨控微型固体火箭。该武器属于常规反卫星系统,是当前必须研究的方向,它的设计思想、方案构成、材料选择等具有独特的新颖性,值得借鉴。     

常增益变采样率自适应滤波

作者曾给出一种跟踪飞机机动的自适应滤波,采用直角坐标系中的9维状态量,利用最优检测统计量探测目标机动,并用估计到的机动加速度修正目标的预报值。 本文将在文献[1]的基础上,提出两种简化自适应滤波。其一,采用两套直角坐标系中的9维卡尔曼滤波,其中的一套滤波用于取代最优检测统计量,完成对目标机动的探测和估计的功能;其二,采用三通道解偶的常增益变采样率自适应滤波。     

具有自动回溯的机动目标航迹精细化分段识别

机动目标航迹分段识别是判断目标行为意图的基础,然而现有航迹分段算法对模式变化检测能力弱,难以满足机动目标航迹快速精细化分段的需求。提出双层精细化航迹分段框架,预分段层检测目标运动过程中的模式切换,确定模式变化明显的预分段区,得到目标模式变化明显区域的预分段点;再分段层对模型差异小的非预分段区航迹进行回溯迭代优化再分段,得到更为精细的分段点。该框架具有从粗到精的航迹分段处理能力,实现了对于机动目标航迹的精细化分段识别。选取两个典型的目标机动仿真场景验证了所提算法的有效性,不仅减少了迭代优化时间,而且提高了分段识别精度。     

自主机动空间绳网机器人设计与动力学建模

设计了一种新型“平台+绳系+柔性网+自主机动单元”结构的空间机器人系统,较强的机动能力和较大的任务范围使其在空间垃圾清理任务中具有显著的优势。详细描述了自主机动空间绳网机器人的概念、任务过程和特点。为了分析自主机动空间绳网机器人逼近目标过程开始时柔性网网型的变化趋势,采用质量集中法建立了逼近过程的动力学模型,模型中将柔性网离散化为无质量弹性杆和质点的集合,同时考虑了自主控制力的作用。在不同条件下对逼近过程中的网型变化进行了数字仿真,仿真结果表明：逼近过程开始时,自主机动单元无控状态下柔性网将产生收口运动,且收口运动的强弱与自主机动单元和柔性网的初速有关;逼近轨迹也将偏离目标方向。通过自主机动单元的自主控制力能够避免收口运动和逼近方向偏移的产生。     

改进的基于隐马尔可夫模型的自适应IMM算法

针对机动目标跟踪中交互式多模型算法(IMM)的马尔可夫转移概率矩阵固定不变造成跟踪精度降低的问题,在已有的基于隐马尔科夫模型(HMM)的自适应IMM算法的基础上,对隐马尔可夫链的长度和Baum-Welch算法迭代次数的2个参数对该算法跟踪性能的影响,进行了深入研究分析,进一步明确了这2个参数选择的依据;并针对该算法在目标机动转换时峰值误差增大的问题,给出了2种修正方法,从而提出了改进的基于HMM的自适应IMM算法。最后,通过仿真分析了算法的参数和修正方法对跟踪性能的影响,并与传统IMM算法进行对比,证明了文章提出算法的有效性。     

线偏差控制的螺旋机动突防与导引一体化设计

导弹机动突防弹道设计与末制导修正能力密切相关,在可用过载与终端角度约束下,提出一种基于线偏差控制的机动突防与导引一体化设计方法。首先,建立了机动突防与导引一体化设计模型;其次,设计了可用过载与终端角度约束的虚拟导引弹道制导律;再次,提出了一种运动过载约束的螺旋机动线偏差指令信号,并设计了相对机动弹道制导律。基于指令滤波和扩张状态观测器分别解决输入受限和干扰估计问题,并基于Lyapunov稳定性理论证明了闭环系统稳定性。不同过载约束下的突防仿真结果表明,所设计的一体化方法能兼顾机动突防与精确打击需求,典型场景下对PAC 3拦截弹的单发突防成功率达到96.2%。     

一种基于径向加速度的Singer-EKF机动目标跟踪算法

针对雷达均不能提供目标加速度信息,在目标机动时会出现跟踪精度差甚至跟踪发散的问题,提出一种基于径向加速度的Singer-EKF算法。该算法在信号处理阶段利用Radon-Ambiguity变换（RAT）估计出目标的径向加速度,并通过坐标转换将其引入量测向量中,然后采用基于Singer模型的扩展卡尔曼滤波（EKF）算法实现机动目标的跟踪。仿真验证了该方法的有效性,并与传统的不带径向加速度的扩展卡尔曼滤波（EKF）方法进行了比较,结果表明该方法在径向距离、位置、加速度和速度估计精度方面都有所提高。     

Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target’s status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target’s predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF’s importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the state-of-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.     

Model predictive control for close-proximity maneuvering of spacecraft with adaptive convexification of collision avoidance constraints

This study investigated model predictive control (MPC) for close-proximity maneuvering of spacecraft. It is essential for a designed MPC to effectively handle collision avoidance between the servicer spacecraft and the client spacecraft, especially while the client is rotating. The rotating motion of the client leads to dynamic changes in the collision avoidance constraints, which increases the difficulty of optimizing the control input in the MPC framework. Therefore, this study presents a method to improve the performance and computational efficiency of MPC for rendezvous and docking with a nonrotating or rotating client. An ellipsoid is adopted to model the client’s keep-out zone (KOZ). Given the spherical KOZ of the servicer, an expanded ellipsoid is introduced to describe the KOZ for the center of mass of the servicer and modeled as a nonlinear constraint. The linearization method for reference points located at the expanded ellipsoid is adopted to convexify the nonlinear constraints. The reference points are adaptively determined according to the positions of the servicer, client, and expanded ellipsoidal KOZ. The resulting hyperplanes are then used to describe the collision avoidance constraints. By utilizing the aforementioned strategies, combined with the calculated reference points, an adaptive convex programming algorithm suitable for real-time implementation of MPC is derived. The performance of the proposed method is demonstrated through numerical simulations.