一种快速的星敏感器星跟踪方法研究

阐述了星敏感器中星跟踪方法的重要性,指出了目前国内外星跟踪方法的不足。针对这些不足,提出了一种全新的、快速的星跟踪方法。新的跟踪方法采用现场可编程门阵列(FPGA)实现了实时的星点定位;正是由于这种技术的采用,加快了星点位置信息的获取,摈弃了跟踪窗的跟踪方法,采用简单的匹配识别的跟踪方法;对于新星的识别,由于有初始姿态而采用匹配组的识别方法。最后给出了星跟踪过程的实验结果。     

基于注意力机制和特征匹配的空对地多目标检测与跟踪算法

多目标跟踪算法是实现无人机自主导航的关键技术，为解决现有方法存在的小目标检测能力弱、计算能耗大、鲁棒性差等问题，提出一种基于注意力机制和特征匹配的多目标空对地跟踪算法，以实现航拍视角下对目标的精准高效跟踪。首先，引入通道可分离卷积，实现目标检测模型的轻量化；其次，构造融合空间注意力机制的小目标检测分支，提高对小微目标的检测精度，最后，优化目标跟踪算法的外观重识别网络，提高多目标跟踪效率。使用Visdrone2019-MOT数据集对所提算法进行验证，实验结果表明，所提算法的MOTA值提高了0.６%，FPS值为21.31帧/s，在模型大小和跟踪精度上实现了较好的平衡。     

基于模型预测静态规划的自适应轨迹跟踪算法

轨迹跟踪是飞机自主运动控制的关键问题之一。跟踪模型预测静态规划（T-MPSP）是一种新近发展的基于非线性模型的轨迹跟踪算法,但对于飞行器受损等情况下,模型参数相较于标称模型具有较大的偏差时,则可能导致轨迹跟踪效果不理想。提出了基于参数估计的自适应轨迹跟踪算法,在模型预测静态规划（MPSP）的框架下得出解析解,实现了对参数的实时估计,据此更新跟踪模型预测静态规划算法中所使用的模型后,可以有效扩大对参数偏离的适应性,并保留模型预测静态规划计算效率高的特点。通过仿真对比得出,相较于已有的跟踪模型预测静态规划,改进后的算法对模型参数偏离的容忍性明显提高,且算法迭代效率高,适于在线应用。     

基于鉴相器的矢量跟踪环设计

基于鉴相器的矢量跟踪环是锁定导航卫星码和载波的一种跟踪算法,这种算法适合于在弱信号环境下进行跟踪。矢量环不仅充分利用信号跟踪和导航状态解算之间的内在耦合关系,而且每一通道中采用鉴相器和锁频环结合的方式,系统一步内完成信号跟踪和导航解算任务。反馈控制量NCO是由导航滤波器和环路内部滤波器共同产生。实验表明,矢量跟踪环具有快速重捕获特性,能够在弱信号的环境下运行,比起传统环路有更小的跟踪误差。     

Loopy belief propagation based data association for extended target tracking

The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements. Recently, the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus. Different from the belief propagation based Extended Target tracking based on Belief Propagation (ET-BP) algorithm proposed in our previous work, a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper. The proposed formulation can be solved by the Loopy Belief Propagation (LBP) algorithm. Furthermore, the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy. Finally, experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency. Additionally, the convergence of the proposed algorithm is verified in the simulations.     

非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法     

单天基平台测向的空间非合作目标跟踪

针对仅含角度测量信息的单个天基平台可观测性较弱的问题,提出了一种含脉冲机动检测的空间非合作目标跟踪算法,并设计了非合作目标实时跟踪数据处理流程.该算法利用抗差估计技术和UKF（Unscented Kalman Filter,无迹卡尔曼滤波）算法构造目标跟踪滤波器,并综合残差多项式拟合和新息分布特征等方法实现目标机动检测,在天基平台观测信息类型有限和观测几何较差的情况下,可以同时排除孤立野值和成片测量野值的影响,实现非合作机动目标的连续稳定跟踪.数值实验验证了算法的可行性和有效性,也表明了跟踪精度和可靠性与测量精度密切相关.     

基于视觉注意机制的认知雷达数据关联算法

针对传统关联波门设计方法在应用于机动目标跟踪时容易引起失跟、以及概率数据关联算法不适于多交叉目标跟踪的问题,提出了一种基于人类视觉选择性注意机制和知觉客体的"特征整合"理论的认知雷达数据关联算法。算法以综合交互式多模型概率数据关联算法为基础,采取假设目标最大机动水平已知的"当前"统计模型和匀速运动模型作为模型集,通过实时交互使关联波门能够随目标机动动态调整,较好地兼顾了雷达计算耗时和跟踪成功率。在利用目标位置特征的基础上,进一步提取、整合目标运动特征,对关联波门交叉区域公共量测进行分类,使多交叉目标跟踪问题转化为多个单目标跟踪问题,优化了传统概率数据关联算法。仿真结果表明：与传统关联波门设计方法相比,算法跟踪失败率和计算耗时明显降低;而且在计算资源增加不大的情况下,杂波环境适应性也得到了显著增强。     

多目标跟踪中自适应时间资源调度

为了提高雷达的工作效率,在改进交瓦多模型-概率数据关联(IMMPDA)跟踪算法的基础上,提出了基于灰色关联度和粒子群优化理论的自适应多目标跟踪的时间资源凋度(ATRS)算法.首先对每个跟踪目标设置不同的期望跟踪精度;然后以灰色关联度作为资源管理模型中的度冒函数和粒子群算法中的适应度甬数,来衡量各种情况下预测跟踪精度与期...     

A fusion and learning algorithm for landing aircraft tracking:compensating for exhaust plume disturbance

An algorithm is presented for tracking a landing aircraft using fusion of two different passive sensors, a laser range finder (LRF) and a forward-looking infrared (FLIR) camera. The main feature of this algorithm is its ability to identify and compensate for an exhaust plume disturbance. The algorithm is based on the extended Kalman filter (EKF) and the filtering confidence function (FCF) which introduces a learning approach to the tracking problem. The results of a simulation using the learning tracking algorithm and the EKF alone are presented and compared     

一种基于参数控制的低轨星载接收机载波跟踪算法CSCD

低轨星座接收机面临大多普勒频移及频繁快速换星等设计约束,对其载波跟踪环路设计提出了较高的动态适应性与跟踪精度要求。针对以上问题,提出了一种基于参数控制的载波跟踪算法。该算法引入环路控制因子参数,将环路滤波器分为牵引和跟踪两阶段。基于理论建模推导环路控制因子的最优参数配置原则,指导实现牵引和跟踪两种状态滤波器的协同配合,在牵引阶段有效引导大多普勒信号快速入锁,在跟踪阶段精确估计载波频移参数,实现基于低轨星载平台的GNSS信号快速准确跟踪。理论与仿真结果均表明基于参数控制的载波跟踪算法能够有效提升环路的动态适应性与跟踪精度,满足低轨星载接收机的设计需求。与传统算法相比,该算法在保证信号跟踪精度的同时,能够将收敛时间缩短78%,且环路设计简单,易于硬件实现。     

一种改进的强跟踪UKF算法及其在SINS大方位失准角初始对准中的应用

针对现有的强跟踪无迹卡尔曼滤波（UKF）算法存在理论依据不足和滤波性能欠佳等问题,从正交性原理出发,通过严谨的推导得到强跟踪UKF成立的充分条件,在此基础上提出一种改进的强跟踪UKF算法。该算法无需求解雅可比矩阵且计算量较小,渐消因子的作用位置以及求解公式均不同于原始的强跟踪滤波器。给出了该算法的流程和渐消因子的求解方法,证明了该算法满足强跟踪滤波器的充分条件,并分析了其渐消因子的作用机理。进行了捷联惯性导航系统（SINS）大方位失准角初始对准仿真,结果验证了所提强跟踪UKF算法的正确性和有效性。     

一种改进的强跟踪 UKF 算法及其在 SINS 大方位失准角初始对准中的应用郭泽,缪玲娟,赵洪松简

 针对现有的强跟踪无迹卡尔曼滤波（UKF）算法存在理论依据不足和滤波性能欠佳等问题,从正交性原理出发,通过严谨的推导得到强跟踪UKF成立的充分条件,在此基础上提出一种改进的强跟踪UKF算法。该算法无需求解雅可比矩阵且计算量较小,渐消因子的作用位置以及求解公式均不同于原始的强跟踪滤波器。给出了该算法的流程和渐消因子的求解方法,证明了该算法满足强跟踪滤波器的充分条件,并分析了其渐消因子的作用机理。进行了捷联惯性导航系统（SINS）大方位失准角初始对准仿真,结果验证了所提强跟踪UKF算法的正确性和有效性。     

Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD

In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like.     

基于成像跟踪的比例导引律实现

在综述了各种成象跟踪算法的基础上，给出了一种新的适于飞行力学自动目标跟踪算法的基本思想和方法步骤。研究和控制利用成像跟踪算法获取比例导引律中视线角速度和导弹怀目标间相对速度的方法，并结合导弹飞行控制数学模型，提出了一种基于成像跟踪的比例导引律实现方法。     

N-fold Bernoulli probability based adaptive fast-tracking algorithm and its application to autonomous aerial refuelling

Recently, deep learning has been widely utilized for object tracking tasks. However, deep learning encounters limits in tasks such as Autonomous Aerial Refueling (AAR), where the target object can vary substantially in size, requiring high-precision real-time performance in embedded systems. This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem. First, an Asymmetric Convolutional Network (ACNet) and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background. The prior object information, such as its location in the previous frame and its speed, is utilized to adaptively track objects of various sizes. Moreover, based on the n-fold Bernoulli probability theorem, we develop a filter that uses statistical laws to reduce the false positive rate of object tracking. To evaluate the efficiency of our algorithm, a new AAR dataset is collected, and extensive AAR detection and tracking experiments are performed. The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks; the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.     

基于输出反馈的柔性航天器变结构跟踪控制方法

为解决柔性航天器姿态机动的控制问题,给出了基于输出反馈的变结构跟踪控制算法。针对柔性航天器的大角度机动,在建立了柔性航天器相对参考轨迹的动力学方程的基础上,设计了仅利用航天器本体的角度和角速度信息的变结构跟踪控制器,使得姿态状态跟踪误差(包括姿态跟踪误差和姿态角速度跟踪误差)以及挠性附件的模态变量从任意的初始状态出发都会到达包含原点的一个闭集内,并且姿态状态跟踪误差能收敛到零,并给出了严格的数学证明。仿真结果证明了所提控制方法的可行性和有效性。     

A sequential detection approach to target tracking

Sequential hypothesis testing is investigated for multiframe detection and tracking of low-observable maneuvering point-source targets in a digital image sequence. The proposed multiple multistage hypothesis test tracking (MMHTT) algorithm extends tracks formed from sequentially detected target trajectory segments using a multiple hypothesis tracking strategy. The MMHTT algorithm does not require a probabilistic larger maneuver model. Computational efficiency is achieved by using a truncated sequential probability ratio test (SPRT) to prune a dense tree of candidate target trajectories and score the detected trajectory segments. An analytical performance evaluation is presented and confirmed by experimental results from an optical satellite tracking application     

Efficient target tracking using dynamic programming

A dynamic programming (DP) algorithm has been developed for the detection and tracking of subpixel-sized, low signal-to-noise ratio (SNR) targets observed by side-or forward-looking imaging sensors. A distinguishing feature of this approach is that target detection and tracking are combined into a single optimization procedure that takes into account statistical models of target motion, background noise, and clutter. Current work has led to a number of technical innovations that improve the performance and efficiency of the DP tracking algorithm, including the development of a new track scoring function, and an extension to the basic DP algorithm that reduces computation requirements by over an order of magnitude. A prototype infrared (IR) target tracking system incorporating these enhancements has been implemented for a step-starting IR camera application. Sensitivity improvements of several decibels over conventional sequential detection and tracking algorithms were realized     

空间多分辨率模糊目标跟踪

提出了一种新的模糊目标跟踪算法--CPDA算法。这个算法在空间多分辨率框架下应用概率数据互联算法,在粗分辨率上实现模糊目标跟踪。在不同虚警密度的模糊目标环境下,利用仿真实验分析了CPDA算法的跟踪性能,同时将其与单分辨率上的联合概率数据互联方法进行了性能比较。仿真结果表明,CPDA算法的跟踪性能在达到与单分辨率上JPDA算法同样性能的条件下,能够以较小的计算量跟踪模糊目标。