A two-step robust adaptive filtering algorithm for GNSS kinematic precise point positioning

In kinematic navigation and positioning, abnormal observations and kinematic model disturbances are one of the key factors affecting the stability and reliability of positioning performance. Generally, robust adaptive filtering algorithm is used to reduce the influence of them on positioning results. However, it is difficult to accurately identify and separate the influence of abnormal observations and kinematic model disturbances on positioning results, especially in the application of kinematic Precise Point Positioning (PPP). This has always been a key factor limiting the performance of conventional robust adaptive filtering algorithms. To address this problem, this paper proposes a two-step robust adaptive filtering algorithm, which includes two filtering steps: without considering the kinematic model information, the first step of filtering only detects the abnormal observations. Based on the filtering results of the first step, the second step makes further detection on the kinematic model disturbances and conducts adaptive processing. Theoretical analysis and experiment results indicate that the two-step robust adaptive filtering algorithm can further enhance the robustness of the filtering against the influence of abnormal observations and kinematic model disturbances on the positioning results. Ultimately, improvement of the stability and reliability of kinematic PPP is significant.     

抗差自适应分步滤波算法在PPP/INS组合导航中的应用

针对常规抗差自适应滤波算法在PPP/INS组合导航应用中存在难以准确识别和分离观测粗差及运动异常对定位结果影响的问题，基于分类因子自适应滤波原理,提出了一种抗差自适应分步滤波算法。该算法首先执行第一步滤波,对状态模型异常信息进行隔离，仅对观测粗差进行诊断和抗差处理；然后在第一步滤波的基础上，执行第二步滤波，对状态模型异常进行诊断和自适应处理。算法分析表明，抗差自适应分步滤波算法可以准确地识别和分离观测粗差和运动异常扰动。实验结果表明，抗差自适应分步滤波算法能够进一步增强滤波算法抵抗观测粗差和运动异常扰动对滤波结果的影响，提高PPP/INS组合导航系统定位结果的稳定性和可靠性。     

Linear FM signal parameter estimation from discrete-timeobservations

The authors consider the problem of estimating the parameters of a complex linear FM signal from a finite number of noisy discrete-time observations. An estimation algorithm is proposed, and its asymptotic (large sample) performance is analyzed. The algorithm is computationally simple, consisting of two fast Fourier transforms (FFTs) accompanied by one-dimensional searches for maxima. The variance of the estimates is shown to be close to the Cramer-Rao lower bound when the signal-to-noise ratio is 0 dB and above. The authors applied the algorithm to the problem of estimating the kinematic parameters of an accelerating target by pulse-Doppler radar. A representative test case was used to exhibit the usefulness of the algorithm for this problem, and to verify the analytical results by Monte Carlo simulations     

Modified Grid Algorithm for Noisy All-Sky Autonomous Star Identification

Grid algorithm is a classical algorithm for all-sky autonomous star identification. However its recognition rate decreases rapidly when the position and magnitude noise increases in the star image. In order to overcome this problem, a modified grid algorithm called the "elastic gray grid algorithm" is proposed. Gray pattern and elastic template matching are introduced into the grid algorithm to enhance its ability to tolerate the position and magnitude noise. Experimental results show that the modified grid algorithm performs better compared with the original one. The elastic gray grid algorithm reached up to a 100% recognition rate under random positional noise in our test bed. Furthermore it increases the original recognition rate from 69.86% to 97%, under the same condition, for both the position and the magnitude noise.     

基于Gauss伪谱法的UCAV对地攻击武器投放轨迹规划

研究无人作战飞机(UCAV)在对地攻击阶段的武器投放轨迹规划问题.针对传统方法在处理复杂的飞行器运动学、动力学约束上存在的困难,提出了一种基于Gauss伪谱法(GPM)的求解策略.首先,为了最大程度地逼近实际飞行环境,对UCAV的气动力特性、发动机推力特性、油耗特性及大气环境特性进行了高精度拟合,并充分考虑了飞行器各种...     

基于Euler／N—S方程的跨音速非线性静气动弹性问题研究

在C-H网格的基础上,采用Jameson的中心差分有限体积法求解Euler/N-S方程,采用结构影响系数法计算结构的弹性变形,用三角元面积加权法和常体积转换法(CVT)实现流固耦合,计算了弹性后掠机翼在跨音速状态的静气动弹性问题     

柔性臂空间机器人的一类运动学方程及轨迹规划

用柔性机械臂连杆末端的弹性变形以及变形角度来表示空间机器人柔性臂的弹性运动变量,克服了用无穷维振动模态变量来表示弹性变形给系统运动学建模带来的困难;基于广义雅可比矩阵的思想,建立了柔性臂空间机器人"双广义雅可比矩阵"形式的运动学模型,该运动学模型描述了柔性臂弹性变形对空间机器人的运动影响;以运动学方程为基础,设计了柔性臂空间机器人的惯性空间内连续轨迹规划算法。仿真表明,规划的机械臂关节运动规律可以补偿柔性连杆振动给机械臂末端位置带来的影响,使机械臂末端位置准确沿着期望的轨迹运动。     

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     

无人艇航行规则及运动学约束下的改进RRT轨迹规划方法

现有的RRT算法没有考虑无人艇的运动特性,难以解决无人艇轨迹规划问题,也没有基于无人艇航行规则来考虑无人艇的动态避碰。针对上述问题,在无人艇航行规则及运动学约束下,提出了改进的双层RRT动态轨迹规划方法。在第一层框架中,改进了探索点及步长选择策略,并结合国际海上避碰规则公约与最短会遇时间建立最优位置窗口来构造四向扩展随机树,从而可以在考虑海事规则的前提下快速搜索出联通路径。在第二层框架中,考虑到无人艇的运动学约束,将上一层的联通路径点作为分段启发点,然后结合速度运动模型来限制无人艇的拐角与转弯半径,并基于速度运动模型得到的弧长计算出每一个节点的代价值,最终在动态障碍物环境中得到一条可行平滑轨迹。仿真与实船实验均验证了该改进算法的有效性,实验表明该改进算法可以有效地解决传统RRT算法离障碍物过近、路径不平滑、不符合无人艇运动学与无人艇航行规则等问题。其中,轨迹转折数目为0,与障碍物最近距离是传统RRT算法的两倍以上,最大转折角度指标远好于传统RRT算法。     

基于改进A^*算法的无人机快速轨迹规划方法

针对旋翼无人机在三维障碍物环境中自主飞行时路径搜索速度慢、轨迹生成通常忽略无人机动力学特性的问题,发展一种基于改进A^*算法并同时考虑无人机动力学特性和运动学性能的快速轨迹规划方法。首先,在三维障碍物环境中运用改进A^*算法通过剔除部分网格节点降低A^*算法的节点计算量,提升算法的路径搜索速度;其次,以最小化飞行轨迹的四阶导数作为目标函数,以路径点处的位置、速度、加速度等各阶导数作为约束条件优化飞行轨迹;最后,在三维障碍物环境中对比A^*算法改进前后的路径搜索结果,并对优化的飞行轨迹进行仿真飞行测试。结果表明:改进A^*算法大幅降低了A^*算法的节点计算量,显著提升了路径搜索速度;且无人机能够始终以较小位置误差沿优化轨迹光滑连续飞行。     

基于Radau伪谱方法的轨迹优化

研究了导弹在末制导阶段满足多约束条件下的轨迹优化问题。针对传统方法在处理复杂的非线性运动学、动力学约束上存在的困难,提出了一种基于Radau伪谱方法(RPM)的求解策略。首先,给出了考虑导弹速度和空气密度变化、终端和过程约束条件以及2种不同的性能指标函数的最优轨迹优化问题;其次,利用RPM将轨迹优化问题转化为非线性规划问题,采用序列二次规划算法求得最优解;最后,分2种初始条件对最大落角和最大终端速度的轨迹优化问题进行了仿真验证,结果表明:本文方法能够在较短的时间内生成一条满足多种约束要求的高精度的最优轨迹。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

弹性高超声速滑翔飞行器的状态/参数联合估计

弹性高超声速滑翔飞行器具有强非线性、强不确定性和刚体/弹性耦合的特点，对其状态和参数进行估计十分必要。为解决这一问题，提出了一种传感器布置策略和一种利用正交三角（QR）分解更新到达代价的滚动时域估计算法（MHE-QR）。首先，建立了考虑弹性的传感器观测模型并分析了传感器位置对可观性的影响，并在此基础上提出了一种反映系统可观性的性能指标。传感器布置策略以此性能指标为目标函数，将传感器布置问题转化为约束非线性优化问题并求解，即可得到最优传感器布置方案。然后提出了MHE-QR算法。在滚动时域估计的框架下，该算法利用前向动态规划原理将到达代价的计算转化为最小二乘问题，并给出了基于QR分解的到达代价更新算法。仿真结果表明该传感器布置策略和MHE-QR算法能够有效提高估计精度、收敛速度和计算速度。此外，MHE-QR算法具有实时应用的潜力。     

Strapdown inertial navigation system algorithms based on dual quaternions

The design of strapdown inertial navigation system (INS) algorithms based on dual quaternions is addressed. Dual quaternion is a most concise and efficient mathematical tool to represent rotation and translation simultaneously, i.e., the general displacement of a rigid body. The principle of strapdown inertial navigation is represented using the tool of dual quaternion. It is shown that the principle can be expressed by three continuous kinematic equations in dual quaternion. These equations take the same form as the attitude quaternion rate equation. Subsequently, one new numerical integration algorithm is structured to solve the three kinematic equations, utilizing the traditional two-speed approach originally developed in attitude integration. The duality between the coning and sculling corrections, raised in the recent literature, can be essentially explained by splitting the new algorithm into the corresponding rotational and translational parts. The superiority of the new algorithm over conventional ones in accuracy is analytically derived. A variety of simulations are carried out to support the analytic results. The numerical results agree well with the analyses. The new algorithm turns out to be a better choice than any conventional algorithm for high-precision navigation systems and high-maneuver applications. Several guidelines in choosing a suitable navigation algorithm are also provided.     

基于实数编码遗传算法的轨迹综合优化设计

提出了一种基于实数编码遗传算法的机构轨迹综合优化设计。在传统二进制编码遗传算法上进行改进,采用具有更快全局寻优能力的实数编码遗传算法对四杆机构各参数进行优化,从而得到机构最优解。运用MATLAB软件实现遗传算法优化设计,结果表明了此方法的有效性。     

Nonlinear bending analysis of a 3D braided composite cylindrical panel subjected to transverse loads in thermal environments

The aim of this study is to investigate nonlinear bending for a 3-Dimensional(3D)braided composite cylindrical panel which has transverse loads on its finite length. By refining a micro-macro-mechanical model, the 3D braided composite can be treated as a representative average cell system. The geometric structural properties of its components deeply depend on their positions in the section of the cylindrical panel. The embedded elastic medium of the panel can be described by a Pasternak elastic foundation. Via using the shell theory of the von Ka′rma′nDonnell type of kinematic nonlinearity, governing equations can be established to get higherorder shear deformation. The mixed Galerkin-perturbation method is applied to get the nonlinear bending behavior of the 3D braided cylindrical panel with a simply supported boundary condition.Based on the analysis of the braided composite cylindrical panel with variable initial stress, geometric parameter, fiber volume fraction, and elastic foundation, serial numerical illustrations are archived to represent the appropriate nonlinear bending responses.     

Track-to-track fusion with dissimilar sensors

An analysis is described of a kinematic state vector fusion algorithm when tracks are obtained from dissimilar sensors. For the sake of simplicity, it is assumed that two dissimilar sensors are equipped with nonidentical two-dimensional optimal linear Kalman filters. It is shown that the performance of such a track-to-track fusion algorithm can be improved if the cross-correlation matrix between candidate tracks is positive. This cross-correlation is introduced by noise associated with target maneuver that is common to the tracking filters in both sensors and is often neglected. An expression for the steady state cross-correlation matrix in closed form is derived and conditions for positivity of the cross-correlation matrix are obtained. The effect of positivity on performance of kinematic track-to-track fusion is also discussed     

Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes

The task assignment problem of multiple heterogeneous unmanned aerial vehicles （UAVs）, concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources （e.g., weapons） onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromo- somes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.     

柔性冗余度机器人改善频率特性的研究

对改善柔性冗余度机器人的频率特性进行了研究。首先分析了影响柔性机器人固有频率的因素,得出了在结构参数不变的情况下可以通过适当调整关节运动参数来提高机器人固有频率的结论。然后分析了机器人的自运动与关节运动参数之间的关系。在此基础上,提出了在保证末端名义运动不变的情况下通过规划柔性冗余度机器人的自运动调整关节运动参数来提高系统的固有频率,以避开动力奇异并改善机器人动态性能的方法,此外给出了相应的优化算法。最后通过数值仿真验证了该方法的有效性。     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.