基于PnP和自适应线性卡尔曼滤波的实时位姿估计

针对实时位姿估计中扩展卡尔曼滤波(EKF)线性化引入非线性误差和依赖已知噪声分布的缺点,提出一种基于PnP的自适应线性卡尔曼滤波位姿估计求解方法。将PnP位姿估计求解策略引入卡尔曼滤波观测方程,通过对动态方程误差统计参数实时估计,自适应调节卡尔曼滤波递推参数。所提算法求解精度高,固定了观测方程的观测向量维度,提高了算法实用性。通过仿真试验,比较了该算法与EKF的位姿估计精度,通过量化误差分析,证明了该方法可以提高三维运动位姿估计精度,也验证了该方法的有效性。     

利用集群内测距和对目标测向的协同定位方法

当多个飞行器间相对位置测量精度显著高于飞行器绝对定位精度时，在飞行器间增加相对测量，对各飞行器的定位测量协同处理，能提高绝对定位精度。本文针对集群飞行器对非合作目标不能测距而仅能测向的情况，通过在集群飞行器间引入相互测距，来提高集群飞行器自身和对非合作目标的定位精度。对于这种不完整相对测量情况，给出了能提高定位精度的协同定位求解方法，包括非线性静态优化估计和线性化后最小二乘估计方法。这种协同定位方案，不需要进行完整的相对位置测量，对测距和测向在群体相对测量中进行合理分解，降低了飞行器测量设备配置的要求。通过仿真验证了该方法的有效性。     

包含乘性噪声自适应修正的非合作目标相对导航算法

无人机（UAV）态势感知的任务是利用机载传感器对未知环境进行目标识别和引导,针对无人机与非合作目标间中远距离的相对导航问题,提出了一种基于角度和距离量测的相对状态估计算法。在现有滤波算法的基础上,为了提高精度和稳定性,本文利用了列文伯格-马夸尔特（LM）优化的思想对迭代卡尔曼滤波（IEKF）算法进行改进,提出了一种LM-IEKF算法,并推导该算法在迭代过程中的状态更新方程及协方差阵的递推公式。在此基础上,考虑到距离传感器由于信号相关特性而引入的乘性噪声,现有的加性噪声模型难以适应,因此,进一步提出了基于量测噪声自适应修正的Modified LM-IEKF方法,通过在线实时更新噪声阵提高滤波的精度,并设置渐消记忆指数平滑估计结果。算法验证结果表明,与现有的EKF、IEKF算法相比,在仅含加性噪声的情况下,LM-IEKF算法具有更好的性能;在包含乘性噪声的情况下,Modified LM-IEKF可以有效地估计量测噪声,与目前广泛使用的EKF算法相比,在综合相对位置和相对速度精度上分别提高了10%和23%。     

基于路标观测角的星际着陆器自主位姿确定技术

针对利用导航路标进行六自由度状态估计这一非线性、模糊性问题,对星际着陆器自主位姿确定技术进行了研究。为了减小算法的复杂性,提高求解精度,基于欧式变换下角度不变性,提出以导航路标观测视线之间所形成的夹角作为观测量,对像素观测方程中位置、姿态状态进行解耦求解。通过对观测矩阵的讨论,分析了导航路标空间分布对位姿确定精度的影响,给出了导航路标选取的最优观测方案。最后利用蒙特卡罗仿真对所提导航算法进行了验证,并对影响导航精度的各相关因素进行了分析。     

基于运动捕捉系统的多旋翼飞行器实时测姿算法

旋翼飞行器的室内自主飞行是目前研究的热点之一。在室内飞行过程中,飞行器姿态和位置信息可以通过运动捕捉系统（Motion Capture System,MCS）来进行实时测量,从而为机载低成本MEMS惯性导航系统提供校正信息。结合四旋翼飞行器的结构特性,提出了一种五点实时测姿算法。相对于目前MCS常用的测姿算法,该算法可以降低标记点安装引起的测姿误差。室内实验结果表明,该算法测姿精度高,并且能够有效实现四旋翼飞行器室内动态实时姿态测量,具有较好的工程应用价值。     

车载单天线GPS/MEMS-SINS组合对准算法

捷联式惯性导航系统（SINS）需要精确的初始姿态信息进行姿态、速度和位置解算,而微机电系统（MEMS）惯性传感器由于精度限制无法完成自对准。为了解决这个问题,提出一种基于单天线GPS测姿和MEMS SINS的组合对准算法。采用单天线GPS在载体稳定协调运动条件下给出粗略的航向角信息,进行姿态粗对准。在精对准阶段,使用GPS提供的载体的航向角、位置和速度信息作为观测量,建立Kalman滤波器,对捷联式惯性系统的误差项进行估计,得到精确的姿态矩阵（DCM）,完成对准。经过仿真和车载试验验证,对准后的俯仰角和横滚角均方误差在0.1°内,航向角均方误差在0.5°以内。表明算法可以在运动条件下完成载体的对准要求,有一定的实用价值。     

基于TD-RLS算法的四旋翼飞行器系统参数辨识

在对飞行器气动参数进行辨识的过程中,常需要对获得的数据求微分或二次微分,而利用差分法求微分会放大噪声的影响,引入滤波器抑制噪声又会产生相位延迟.针对这一问题,提出了一种跟踪微分器-递推最小二乘(TD-RLS)辨识算法.首先,建立了悬停条件下四旋翼飞行器的系统模型;然后,基于实验室四旋翼平台飞行试验实测数据,将TD-RLS算法应用于飞行器参数辨识.最终的辨识结果表明,在四旋翼飞行器悬停或者小角度飞行条件下,该方法可以实时获得比较精确的系统模型.     

惯性组合导航系统的实时多级景象匹配算法

 针对景象匹配辅助惯性组合导航系统需要快速准确获取飞行器位置、航向偏差的要求,提出一种实时多级景象匹配算法。算法分为两级,第一级粗匹配中提出了中心点4邻域的抗变形算法,能抗旋转和小尺度变化的影响,使定位精度达到像素级;第二级精匹配中提出了基于分支特征点,应用最小二乘法原理精确匹配定位出两幅图像间的最优相似变换参数,即飞行器的精确位置和航向偏差的算法。仿真分析表明,提出的实时多级算法能满足景象匹配辅助惯性组合导航系统实时性、精确性和鲁棒性的要求。     

基于FPGA的超声三维坐标测量系统

提出基于超声波测距的三维坐标测量方法,利用FPGA实现超声波测距系统电路,设计并行四个通道信号处理系统,获得超声发射端到四个超声接收端的距离,并利用最小二乘法实现发射端空间位置的准确估计。对基于超声波测距的三维坐标测量系统进行实验研究,实验结果表明采用峰值测距和最小二乘三维定位算法可有效实现三维坐标测量,坐标的绝对误差在30 mm以内。     

视觉辅助的无人机自主空中加油建模与仿真

为准确获得插头锥套式空中加油过程中无人机与锥套的相对位姿信息,提出了机器视觉辅助的插头锥套式无人机自主空中加油仿真方案,开发了基于Open CV,Simulink及Vega Prime视景仿真技术的闭环仿真系统.研究了机器视觉识别跟踪锥套的图像处理算法,利用Kalman滤波算法估计无人机与锥套的相对位姿.仿真结果表明,机器视觉算法可以准确识别跟踪锥套,滤波器状态估计的收敛速度较快,无人机姿态角变化范围较小,保证了插头锥套式无人机自主空中加油的平稳安全对接.     

Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach

This paper investigates a formation control problem of fixed-wing Unmanned Aerial Vehicle (UAV) swarms. A group-based hierarchical architecture is established among the UAVs, which decomposes all the UAVs into several distinct and non-overlapping groups. In each group, the UAVs form hierarchies with one UAV selected as the group leader. All group leaders execute coordinated path following to cooperatively handle the mission process among different groups, and the remaining followers track their direct leaders to achieve the inner-group coordination. More specifically, for a group leader, a virtual target moving along its desired path is assigned for the UAV, and an updating law is proposed to coordinate all the group leaders’ virtual targets; for a follower UAV, the distributed leader-following formation control law is proposed to make the follower’s heading angle coincide with its direct leader, while keeping the desired relative position with respect to its direct leader. The proposed control law guarantees the globally asymptotic stability of the whole closed-loop swarm system under the control input constraints of fixed-wing UAVs. Theoretical proofs and numerical simulations are provided, which corroborate the effectiveness of the proposed method.     

无人机空中冲突探测与避撞研究

近年来,无人机运输业迅猛发展,其飞行过程中的冲突探测与避撞问题成为亟需解决的关键问题。在无人机周围建立合理的三维空间模型,优化包括紧急避撞区域、一般避撞区域、监视及提前避撞区域的三级避撞区域系统,并利用ADS-B报文提供的无人机位置、速度等信息,基于无人机一般二维平面上的冲突探测与避撞算法,通过增加垂直方向上的冲突识别来改进冲突探测算法,对比调速、调向两种避让方案在各避撞区域的成功率。结果表明：改进算法能够在无人机数量大幅增加的情况下有效识别冲突无人机,同时采用先调速后调向的避让方案,使避撞成功率达到99.75%,为保障无人机的飞行安全提供有效策略。     

基于压力控制器的气压高度表自动检定系统

通过对航空气压高度表的工作原理和检定方法进行分析,提出了一种新的气压高度表检定方法。该方案采用广泛应用的PPC2气体压力控制器模拟产生精密气压高度信号,直接对航空气压高度表、航空大气数据计算机的静压和气压高度等项目进行检定,对具有RS232数据输出功能的电子式气压高度表可以实现自动检定,对这种方法的测量不确定度进行了分析。该方法具有操作便捷、可靠性高的特点。     

Recursive 3-D motion estimation from a monocular image sequence

Consideration is given to the design and application of a recursive algorithm to a sequence of images of a moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be smooth in the sense that it can be modeled by retaining an arbitrary number of terms in the appropriate Taylor series expansions. Translational motion involves a standard rectilinear model, while rotational motion is described with quaternions. Neglected terms of the Taylor series are modeled as process noise. A state-space model is constructed, incorporating both kinematic and structural states, and recursive techniques are used to estimate the state vector as a function of time. A set of object match points is assumed to be available. The problem is formulated as a parameter estimation and tracking problem which can use an arbitrarily large number of images in a sequence. The recursive estimation is done using an iterated extended Kalman filter (IEKF), initialized with the output of a batch algorithm run on the first few frames. Approximate Cramer-Rao lower bounds on the error covariance of the batch estimate are used as the initial state estimate error covariance of the IEKF. The performance of the recursive estimator is illustrated using both real and synthetic image sequences     

双目视觉绳系支撑飞行器模型位姿动态测量

绳牵引并联机器人（WDPR）为风洞试验提供了一种新型支撑方式,可用于多/六自由度风洞复杂动态试验。针对该支撑下飞行器模型的大范围运动,发展了一种基于双目视觉的模型位姿动态测量方法。首先,设计了一种编码合作标志点,合理布置于模型表面,通过图像处理消除绳对标志点成像干扰,进行标志点三维重构;然后,利用绝对定姿算法求解相对位姿初值,且给出了理论误差分析,并基于双目相机重投影误差构建李代数下的无约束最小二乘优化问题,采用L-M算法进行位姿优化;最后,采用该测量系统分别进行了静态和动态精度验证试验,以及大迎角俯仰振荡等3种单/多自由度典型运动轨迹测量。试验数据显示,静态角度和位移测量精度分别优于0.02°/0.02 mm;动态测量时角度精度可达到0.1°量级,位移平均误差为0.4 mm。研究结果表明：设计的双目视觉测量系统是有效可行的,可为后续风洞试验的实际应用提供支持。     

A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper, we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate, real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs) in GPS-denied environments. The system builds atop a factor graph, and only onboard sensors and computing power are utilized. Benefiting from the keyframe strategy, each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data. The com...     

Three-dimensional analysis of relationship between relative orientation and motion modes

Target motion modes have a close relationship with the relative orientation of missile-totarget in three-dimensional highly maneuvering target interception. From the perspective of relationship between the sensor coordinate system and the target body coordinate system, a basic model of sensor is stated and the definition of relative angular velocity between the two coordinate systems is introduced firstly. Then, the three-dimensional analytic expressions of relative angular velocity for different motion modes are derived and simplified by analyzing the influences of target centroid motion, rotation around centroid and relative motion. Finally, the relationships of the relative angular velocity directions and values with motion modes are discussed. Simulation results validate the rationality of the theoretical analysis. It is demonstrated that there are significant differences of the relative orientation in different motion modes which include luxuriant information about motion modes. The conclusions are significant for the research of motion mode identification,maneuver detection, maneuvering target tracking and interception using target signatures.     

Trajectory Planning Algorithm Based on Quaternion for 6-DOF Aircraft Wing Automatic Position and Pose Adjustment Method

 A 6-degree of freedom (6-DOF) aircraft wing position and pose automatic adjustment method is presented to improve ARJ21 wing-fuselage connection precision and efficiency. Wing position and pose are adjusted by three pillars which are driven by six high-precision servo motors. During the adjustment process, wing is tracked and positioned by laser tracker. Wing initial posi-tion and pose are calibrated by using the measurement coordinates of assembly reference points. Wing target position and pose are calculated according to wing initial, fuselage position and pose, and relative position and pose requirements between wing and fuselage for the connection. Combining Newton-Euler method with quaternion position and pose analyzing method, the inverse kinematics of servo motors, together with the adjustment system dynamics is obtained. Wing quintic polynomial trajec-tory planning algorithm based on quaternion is proposed; the initial, target position and pose need to be solved and the inter-mediate moving path is uncertain. Simulation results show that the adjustment method has good dynamic characteristics and satisfies engineering requirements. Preliminary engineering application indicates that ARJ21 wing adjustment efficiency and precision are improved by using the proposed method.     

基于量子行为鸽群优化的无人机紧密编队控制

随着军事、民用需求的提高和相关领域的技术推动，无人机编队协同作业已成为当今人们逐渐关注的焦点。无人机紧密编队是指无人机间侧向距离在1~2倍翼展内的编队，因其可有效改善编队中无人机的气动性能而备受瞩目。基于无人机紧密编队条件下的气动耦合效应，建立三维空间下的状态空间方程描述双机相对运动，并推导了紧密编队条件下的双机最优编队构型，在此构型下将人工势场法和编队控制相结合作为控制系统中的间接控制环，并针对基本鸽群优化算法的寻优缺陷利用量子行为规则对其进行改进，将改进后的鸽群优化算法和无人机控制量结合作为控制系统中的直接控制环，最后通过仿真对比验证了此控制系统对于紧密编队控制的有效性。     

Optimal Cooperative Sensing using a Team of UAVs

The authors investigates the joint optimal estimation of both the position and velocity of a ground moving target (GMT) using pulse Doppler radars on-board unmanned aerial vehicles (UAVs). The problem of cooperative estimation using a UAV team and the optimization of the team's configuration to achieve optimal GMT position and velocity estimates are addressed. Based on the Cramer-Rao bound, the minimum achievable error variance of the GMT position and velocity estimates is derived. The expression of the minimum achievable estimation error variance for unbiased estimation provided by the Cramer-Rao bound is minimized yielding the optimal configuration of the UAV team. Our solution is complete in that it addresses various GMT tracking scenarios and an arbitrary number of UAVs. Optimal sensor geometries for typical applications are illustrated