地磁姿态检测系统弹体摆动误差 分析与补偿方法

针对地磁/GPS组合姿态检测系统测量精度受弹体摆动影响较大的问题,在分析地磁/GPS组合姿态检测系统的弹体摆动误差的基础上,提出了基于地磁陀螺组合的姿态检测方法,建立了地磁陀螺组合姿态检测模型,利用两轴MEMS陀螺测量的角速率实时积分求解弹体偏航角,结合地磁模块输出的三维地磁分量,组合求解弹体姿态信息.结果表明,与地磁/GPS组合方案相比,增加陀螺模块可消除滚转角和俯仰角随弹体摆动而产生的误差波动,测姿能够适应各种运动环境变化,并保持良好的稳定性.     

GPS测姿系统仿真实验研究

众所周知,海上、空中、陆地上的运动物体往往需要知道其姿态,并根据其姿态进行实时控制。采用惯性系统来实现的测姿,设备复杂,价格昂贵,难以推广,而研究的以GPS接收机为基础的多天线姿态测定系统正克服了这些缺点。着重阐述了单历元GPS测姿算法,通过提取两个独立的GPS OEM传感器来接收信号进行差分计算,从原始数据消除误差后解算出载体的指向。这种由两块单频接收机和信号处理器实现的差分测姿系统有很好的应用前景。     

MEMS陀螺随机误差辨识与降噪技术

针对MEMS陀螺精度受随机误差的影响,并随测姿积分算法逐渐增长的问题,采用时间序列分析法对MEMS陀螺的输入输出数据进行辨识,建立系统的ARMA模型,利用卡尔曼滤波方法对随机误差进行降噪处理,最后对随机误差降噪技术进行实验验证.实验结果表明,降噪后随机误差方差为降噪前的7.17%,由降噪后的MEMS陀螺数据计算得到的姿态角误差小于1°,能够满足低成本导航系统的需要.     

A new method for the combination of GPS and MEMS inertial sensors

提出了一种新的GPS/MEMS微惯性器件组合方法,并根据组合结构的需求.设计了基于载体机动模型和卡尔曼滤波器的GPS信息滤波算法来获取由于载体轨迹机动引起的加速度,从而对基于MEMS微惯性器件的姿态测量算法进行载体机动性补偿,得到的姿态信息对GPS信号失锁不敏感,避免了传统GPS/INS组合方式在无GPS辅助时由于MEMS器件精度低而导致的姿态误差快速、无限增长的问题,而且运算量小,适合在微小型系统上实现.跑车试验表明,该新组合算法与传统GPS/INS组合相比,姿态精度略有下降,但远好于未作机动性补偿的MEMS微惯性器件的姿态测量算法.     

横滚角对P3P位姿测量方法鲁棒性的影响分析

从工程应用的角度出发,在三个控制点构成等腰三角形且仅考虑图像坐标检测误差的条件下,研究了横滚角对P3P位姿测量方法鲁棒性的影响.首先在理论上推导出位姿参数误差与图像坐标检测误差间的关系,在此基础上通过简化条件下的理论推导结合一般条件下的统计分析得到如下结论:当横滚角为0°或±90°时,测量位姿的鲁棒性较好,而当横滚角为±45°或±135°时,测量位姿的鲁棒性最差.这一结论对位姿测量系统的设计具有一定的指导意义.     

基于MEMS惯性传感器的微型姿态测量系统

提出了一种基于低成本MEMS惯性传感器的微型姿态测量系统,包括MEMS速率陀螺、MEMS磁强计、单轴MEMS加速度传感器.重点研究了基于扩展Kalman滤波（EKF）的姿态估计创新算法,通过速率陀螺更新误差状态四元数计算姿态角,并通过飞行方向的加速度传感器和三轴磁强计来补偿陀螺漂移和姿态角误差,利用扩展卡尔曼滤波方程消除瞬时干扰,实现高动态姿态测量.系统的仿真和高动态实验表明,姿态测量动态精度低于5°,静态精度低于0.7°.     

微型航姿系统中三轴MEMS加速度计组合误差建模方法

根据微机械(MEMS)加速度计的测鲢原理对其误差源进行了分析,并研究了微型航姿系统中加速度计的非正交零偏以及温度特性.基于加速度计输出电压随温度变化的规律,利用分温度段对加速度计进行六位置标定的方法建立加速度计的误差模型,并应用于微型航姿系统,实现加速度计的实时补偿.多次实验结果表明,加速度计的误差模型校正了加速度计的...     

GPS在船舶姿态测量中的应用

在本文介绍的测姿方案中,采用GPS接收机对船舶载体姿态测量应用方面进行了研究。其研究方向主要集中在对船舶载体姿态进行测量,并利用现有试验条件进行了尝试,确定船的偏航角、横滚角、俯仰角,最终确定船舶的姿态与时间的关系。目前,建立在相对定位基础上的GPS导航技术己经成熟,随着载波相位动态跟踪技术的发展,以此为基础的GPS测姿系统将逐渐显现出其巨大的潜力和优越性。因此,其应用前景是十分广阔的。     

基于MEMS陀螺仪和加速度计的自适应姿态测量算法

针对MEMS陀螺仪以及加速度计传感器单独测姿时,传感器数据中存在复杂噪声和测量误差导致测量得不到最优姿态角的问题,设计了一种基于MEMS陀螺仪和加速度计的自适应姿态测量算法.算法采用扩展卡尔曼滤波方法实现数据融合,并且在利用Allan方差估计MEMS陀螺动态噪声的同时,加入了遗忘因子和限定记忆的算法思想,从而实时地跟踪数据的量测噪声,实时修正角度估计误差,有效地提高了姿态测量系统的精度.实验结果表明,二者组合定姿可实现高精度的姿态测量,验证了算法良好的动态噪声抑制能力,提高了系统对环境变化的适应性.     

室内GPS发射器角度校准装置的设计与分析

室内GPS利用测量的水平角和垂直角来定位,其测角误差直接影响到系统的综合测量误差,结合室内GPS的测角误差,提出了室内GPS角度校准装置的技术要求,并分别对俯仰角校准和水平角校准所需准确度进行估计,最终利用新型同轴调整装置结合精密转台和竖轴激光导轨研制了一套室内GPS发射器角度校准装置,试验数据表明该装置的俯仰角测量误差小于0．9″,水平角测量误差小于1．6″,解决了室内GPS发射器水平角和俯仰角的校准问题。     

基于奇异谱分解的微机械加速度计振动噪声抑制方法

微机械（MEMS）惯性传感器成本低的同时噪声较大,易受振动信号的干扰。为了利用微机械惯性传感器构成低成本姿态估计系统,提出了一种基于奇异谱分解（SSA）的振动噪声预处理方法。SSA方法的实质是利用延迟扩维矩阵进行主成分分析,其延迟相关的算法能够有效地分离出加速度计测量值中的趋势项与周期项,趋势项中包含有需要的姿态变化信号,周期项即为低频振动噪声,根据过零点检测方法提取出趋势项,将该趋势项作为加速度计的测量值,即可实现对振动噪声信号的抑制,有效地提高姿态估计精度。实际的跑车实验验证了本方法的可行性和有效性。     

基于温控方案的一种MEMS惯性测量装置设计

以提高MEMS惯性测量装置(MIMU)的测量精度为目的,本文设计了一套温度控制系统和控制算法;通过对MIMU进行精密温控,改善了惯性器件的热环境,抑制了外界温度变化对MIMU的干扰,提高了MIMU的综合性能;测试结果验证了方案的有效性。     

一种基于人工鱼群算法的微机电系统加速度计静态修正方法

微机电系统加速度计是惯性跟踪系统的一部分,用来获得运动的位置信息。为了对运动姿态进行精确定位,必须对它的误差进行修正。根据加速度计误差来源和产生机理建立了误差模型,通过分析误差模型,提出了一种基于人工鱼群算法修正加速度计静态误差的方法,并与六位置测试法比较。前者在一定程度上降低了误差,提高了测量角精度,使微机电系统加速度计输出能够准确反映出真实值。     

基于MEMS的分散式低阶AHPRS系统设计

设计一种基于MEMS陀螺、加速度计、磁强计以及GPS模块姿态航向位置参考系统(AHPRS).首先,姿态航向参考系统主要由姿态估计卡尔曼滤波器与补偿卡尔曼滤波器构成,通过补偿滤波器周期修正姿态估计滤波器,从而弥补了由于机体的刚体运动而导致姿态角的估计误差;其次,采用分散式卡尔曼滤波器的设计思路,以估计的误差姿态角作为导航系统卡尔曼滤波器的输入量,有效降低了导航滤波方程的阶次,减小了对姿态解算计算机的性能要求;最后,通过仿真与飞行试验验证该AHPRS有效地克服了动态环境下对系统姿态估计偏差大的缺点,提高了系统的姿态航向与速度位置估计精度.     

基于MEMS陀螺的低成本捷联惯导系统设计

介绍了一种基于MEMS陀螺和石英挠性加速度计的低成本捷联惯性导航系统的设计与实现方法。给出了惯性测量单元(IMU)的模型方程,并在全温下对IMU的输出进行补偿;采用"四元数"法进行姿态计算,通过坐标变换、积分运算确定载体的速度、位置;对惯测样机进行了60 s的静态测试,结果表明该系统短期准确度满足SINS/GPS组合导航系统需求。     

Analysis and Modeling of Inertial Sensors Using Allan Variance

It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They are basically very similar and primarily differ in that various signal processings, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest is the Allan variance. The Allan variance is a method of representing the root means square (RMS) random-drift error as a function of averaging time. It is simple to compute and relatively simple to interpret and understand. The Allan variance method can be used to determine the characteristics of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of error terms in the inertial-sensor data by performing certain operations on the entire length of data. In this paper, the Allan variance technique will be used in analyzing and modeling the error of the inertial sensors used in different grades of the inertial measurement units. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data. Being a directly measurable quantity, the Allan variance can provide information on the types and magnitude of the various error terms. This paper covers both the theoretical basis for the Allan variance for modeling the inertial sensors' error terms and its implementation in modeling different grades of inertial sensors.     

A Small Low-Cost Hybrid Orientation System and Its Error Analysis

Inertial orientation systems have many potential uses beyond air- and spacecraft applications. Compared to the conventional large-scale system, the novel aspects of an orientation system based on micro electromechanical systems (MEMS) are small size, lightweight, low-power consumption, and low cost by the inclusion of microsensors with on-chip functionality, such as micromachined rate gyroscopes, accelerometers, and etc. However, the performance associated with these low-cost MEMS sensors is generally not satisfied. A conventional strapdown method based on integral of angular rate to figure out attitudes will inevitably induce long-term drift. For eliminating this accumulative error and thus using the low-cost navigation system for a long-duration mission, a hybrid configuration by combining a MEMS-based azimuth-level detector with the conventional strapdown system is proposed in this paper. The azimuth-level detector is composed of three-axis MEMS accelerometers and three-axis MEMS magnetometers. With an absolute solution based on the gravity and the earth magnetic field rather than the integral algorithm, attitude and heading estimation without drift errors can be obtained so as to facilitate adjusting the attitude and heading for the conventional strapdown system. The hierarchical computation process for estimating the attitude and heading using the hybrid system is depicted first. Then, comprehensive system errors including transformation errors of the computation, such as skew, scale, and drift errors, are analyzed. Finally, it is verified by both of formula analysis and experimental test that the accumulative errors can be effectively eliminated via this hybrid scheme.      

基于微机电系统与径向基函数的水下航行器姿态检测

为实现水下航行器自主运动控制,准确掌握水下航行器的运动姿态至关重要。微机电系统惯性传感器因具有体积小、成本低和功耗低等特点,被广泛应用于姿态检测系统当中。由于海底的工作环境复杂,故水下航行器在实验过程中设定有一定的非合作性;因此设计采用径向基函数神经网络算法对采集数据进行训练与分类。实验结果表明,微机电系统惯性传感器结合径向基函数神经网络算法对水下航行器的基本行为识别的平均召回率可达94%。     

Optimizing of ANFIS for estimating INS error during GPS outages

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage.