卫星拒止情况下低精度惯导系统航姿算法研究

在卫星拒止情况下,低精度MEMS惯导系统由于惯性器件性能较差,无法长时间保持姿态精度。从重力矢量及飞行器的动力学特性出发,提出了一种基于动态检测和Kalman数据融合的航姿算法。该算法从导航与飞控一体化的理念出发,实时判断飞行器机动和飞控状态,在低动态时利用Kalman滤波器对水平加速度和惯性解算的姿态角进行数据融合,估计和修正水平姿态误差,从而提高水平姿态精度。经过飞行仿真验证,该算法可有效完成飞行器的动态检测,并保证在系统机动情况下水平姿态误差在2°以内。     

一种用于仿生导航无人机航姿求解的混合滤波方法

针对现有组合导航系统易被干扰欺骗以及姿态求解精度不足的问题，设计了惯性测量单元(IMU)与偏振光传感器组成的航姿参考系统(AHRS)。同时，考虑到传统的姿态求解方法精度不高，提出了一种用于仿生导航无人机航姿求解的混合滤波方法。将Mahony滤波后的姿态值作为系统观测量，再结合扩展卡尔曼滤波(EKF)实现传感器数据的深层融合，以获得高精度的姿态角信息。实验结果表明：在静态环境下采用混合滤波方法求解的姿态值能有效滤除偏振光传感器和加速度计内部噪声干扰，其稳定性明显优于两种方法各自求解时的情况；在动态实验中该方法能有效抑制单独采用Mahony滤波时存在的超调问题，表现出更高的动态解算精度，从而为偏振光组合导航系统提供了更精确的姿态估计信息。     

基于EKF的小型涵道式无人机航姿系统算法研究

针对小型涵道式无人机,设计了一种基于扩展Kalman滤波的航姿估计算法。采用四元数作为状态变量,利用陀螺仪的输出构成状态方程,通过加速度计与磁阻传感器构造观测方程来进行扩展Kalman滤波解算。相对于通常的航姿估计算法,这种算法减少了运算量,更适合于基于嵌入式微处理器的航姿估计。实验结果表明这种算法有效的抑制了航姿系统的各种噪声与干扰,同时保证了姿态输出的精度与实时性,可以为小型无人机提供准确的航姿信息。     

基于改进容积卡尔曼滤波的奇异避免姿态估计

 利用矢量进行卫星姿态估计可以归结为非线性滤波问题。为了提高卫星姿态估计的精度,利用龙贝格-马尔塔(LM)迭代算法改进了容积卡尔曼滤波(CKF)。继而,提出改进容积卡尔曼滤波与四元数结合的容积四元数估计器(CQE),有效地避免了卫星大角度机动出现的奇异现象。进一步,给出了一种与影子修正罗德里格参数切换的容积修正罗德里格参数估计器(CME)。仿真对比表明,初始误差较大时容积修正罗德里格参数估计器具有更好的收敛速度和鲁棒性。     

基于星敏感器的卫星姿态估计方法研究

以星敏感器姿态信息作为测量数据,结合卫星姿态动力学方程组,采用增广卡尔曼滤波方法对无陀螺卫星姿态进行估计。研究发现,干扰力矩的变化会降低卫星姿态估计结果的精度,为此提出两种改进的姿态估计算法:增广自适应卡尔曼滤波方法和增广强跟踪卡尔曼滤波方法;仿真表明,两种算法都能很好地克服干扰力矩变化导致的精度下降现象。     

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。     

带有加权基线长约束的GPS/BDS单历元姿态解算算法研究

研究基于GNSS信号的实时载体动态姿态测量算法具有重要意义,多频多模姿态测量算法是目前的研究热点和难点。本文提出一种基于GPS L1/L2和BDS B1三频点观测数据进行联合定姿的新算法,可实现单历元整周模糊度解算,且该算法对周跳不敏感,克服了单频点定姿可靠性差、精度低的缺点。针对多频多模观测时天线相位中心的差异性问题,采用基于加权基线长度约束和上下边界函数的方法实现整周模糊度搜索空间的压缩,提高了整周模糊度解算的效率。最后基于实际测试数据评估定姿精度,实验表明,采用L1/L2/B1三频点联合估计,航向角和俯仰角的精度可达0.1?/m和0.2?/m,比单频观测的精度高约2倍。     

基于单天线GPS的伪姿态测量算法研究

介绍了根据单天线GPS速度测量值测定载体伪姿态的基本算法原理,建立了卡尔曼滤波方程,改进了伪航向角的计算和卡尔曼滤波模型,并进行了算法仿真。结果表明,在载体进行中高速飞行时,单天线测姿算法具有较高的精度,基本满足常规姿态测量要求。     

某型机载惯性/卫星/大气多信息融合MEMS航姿系统的设计与实现

针对机载MEMS航姿系统中器件精度低且易受干扰导致其姿态性能降低的问题,提出了一种基于大气/卫星信息辅助的航姿系统融合方案。构建了多源传感信息辅助下的综合航姿系统方案,所设计系统具有多种工作运行模式,可根据传感器可用状态实现滤波器的无缝切换,建立了组合导航系统状态和量测模型,采用Kalman滤波方法实现多源信息的融合与估计,并开展了原理样机的跑车试验。试验结果表明,所设计的融合方案能有效保障航姿系统的可靠性与精度,具有较高的工程应用价值。     

四元数法在姿态测量中的应用研究

介绍了捷联惯性航姿系统在姿态测量方面的应用。为改进捷联惯性航姿系统计算机解算的实时性,引入了四元数微分方程的三阶泰勒展开式作为系统姿态更新算法,并对某陆地产惯性航姿系统的测量数据用Ｃ语言编制了相应的仿真程序,为研制捷联惯性系统算法提供了参考依据。     

An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration

A current statistical model for maneuvering acceleration using an adaptive extended Kalman filter(CS-MAEKF) algorithm is proposed to solve problems existing in conventional extended Kalman filters such as large estimation error and divergent tendencies in the presence of continuous maneuvering acceleration. A membership function is introduced in this algorithm to adaptively modify the upper and lower limits of loitering vehicles' maneuvering acceleration and for realtime adjustment of maneuvering acceleration variance. This allows the algorithm to have superior static and dynamic performance for loitering vehicles undergoing different maneuvers. Digital simulations and dynamic flight testing show that the yaw angle accuracy of the algorithm is 30% better than conventional algorithms, and pitch and roll angle calculation precision is improved by 60%.The mean square deviation of heading and attitude angle error during dynamic flight is less than3.05°. Experimental results show that CS-MAEKF meets the application requirements of miniature loitering vehicles.     

基于改进UKF的组合导航系统航向角估计方法研究

为满足运载体长航时、高精度的导航需求,解决系统可观测性弱导致的航向角易发散的问题,提出了一种基于MEMS非线性组合导航系统的用于提高航向角估计精度的算法。通过采用单天线GNSS航向角作为量测量进行航向约束,解决了MEMS-SINS/GNSS姿态估计中航向角可观测性弱、估计值收敛差的问题;通过转弯判断规则和常规无迹卡尔曼滤波改进算法,抑制了偏流角对系统估计精度的影响。仿真结果表明,该算法有效地抑制了航向角估计精度差的问题,水平姿态精度达到0.01°,航向角精度达到0.1°,提高了系统的导航精度及可靠性。     

用于微机械捷联式航姿系统的四元素算法卡尔曼滤波器

介绍了一种卡尔曼滤波器 ,它适用于由微机械惯性传感器构成的捷联式航姿系统。文中阐述了系统构成和原理 ,基于四元素算法公式推导了姿态算法和系统误差模型 ,并设计了实时卡尔曼滤波器。仿真结果表明 ,当没有横向加速度干扰时系统精度优于 0 .0 4度 ,当出现 0 .1g/1Hz的横向交变加速度干扰时 ,精度降为 0 .4 4度。初步测试结果表明系统的静态精度为 :俯仰和横滚 /-0 .2度 ,航向 /- 0 .3度。     

一种新型模糊自适应Kalman滤波器在组合导航中的应用

针对光纤陀螺捷联惯导(FOG SINS)/GPS组合导航系统实际工作环境中,由于系统噪声与量测噪声模型发生变化而带来的滤波器发散的问题,提出一种新型模糊自适应Kalman滤波器(FSHAKF).通过引入IMU精度因子与GPS水平精度因子,构造模糊推理系统(FIS),实时更新自适应参数,有效地解决了传统Sage-Husa自适应滤波器(SHAKF)估计模型不准确、系统噪声与量测噪声无法同时估计以及滤波器长时间易发散的问题.仿真实验表明,本文提出的FSHAKF算法相较于SHAKF算法,估计精度得到明显提高,且避免了滤波器的发散.     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.     

A Decision?Directed Adaptive Tracker

In the design of a tracking filter for air traffic control (ATC) applications, a maneuvering aircraft can be modelled by a linear system with random noise accelerations. A Kalman filter tracker, designed on the basis of a variance chosen according to the distribution of the potential maneuver accelerations, will maintain track during maneuvers and provide some improvement in position accuracy. However, during those portions of the flight path where the aircraft is not maneuvering, the tracking accuracy will not be as good as if no acceleration noise had been allowed in the tracking filter. In this paper, statistical decision theory is used to derive an optimal test for detecting the aircraft maneuver; a more practical suboptimal test is then deduced from the optimal test. As long as no maneuver is declared, a simpler filter, based on a constant-velocity model, is used to track the aircraft. When a maneuver is detected, the tracker is reinitialized using stored data, up-dated to the present time, and then normal tracking is resumed as new data arrives. In essence, the tracker performs on the basis of a piecewise linear model in which the breakpoints are defined on-line using the maneuver detector. Simulation results show that there is a significant improvement in tracking capability using the decision-directed adaptive tracker.     

机动目标“当前”统计模型与自适应跟踪算法

本文提出机动目标“当前”统计模型的概念并建议用修正的瑞利-马尔科夫过程描述目标随机加速机动的统计特性。文中指出了在机动目标运动模型中状态（机动加速度）估值与状态噪声之间的内在联系。在此基础上提出了具有机动加速度均值及方差自适应的卡尔曼滤波算法。对一维和三维的情形进行了计算机模拟。计算结果表明,在仅对目标位置进行观测的情况下,这类自适应估值算法无论对高度机动或无机动的目标均可绘出较好的位置、速度及加速度估值。     

Automotive-grade MEMS sensors used for general aviation

The attitude heading reference system (AHRS) provides data for primary flight instruments, head-up displays, autopilots, and moving map navigation systems. Advances in solid-state MEMS rate sensors, coupled with Kalman filter algorithms designed to mitigate high drift rates, provide the basis for low-cost, high-performance AHRS for general aviation. This paper describes the performance of a low cost, miniaturized AHRS using automotive-grade MEMS sensors. The performance of the system is detailed. The implications for certification of this class of system and fault tolerance are discussed.