自适应增量 Kalman 滤波方法

提出自适应增量Kalman滤波(AIKF)的概念和定义,建立自适应增量Kalman滤波模型及其分析方法,给出主要的计算步骤.传统自适应Kalman滤波(AKF)方法能够对事先未知的系统噪声和量测噪声的统计量进行有效的估计.但是,传统自适应Kalman滤波方法也无法对由于环境因素(如深空探测)的影响、测量设备的不稳定性等原因产生的未知时变测量系统误差进行补偿和校正,从而产生较大的滤波误差,甚至导致发散.提出的自适应增量Kalman滤波方法不但能够对系统噪声和量测噪声的统计量进行估计,而且还能成功消除这种测量系统误差,有效地提高滤波精度.该方法计算简单,便于工程应用.      

基于神经网络的航天器GPS/INS组合定姿系统

基于GPS和惯性技术的组合导航系统是近年来导航系统的研究热点和主要发展方向.目前基于卡尔曼滤波方法的算法在稳定性、计算量、算法鲁棒性以及系统可观测性等方面仍然存在问题.基于神经网络技术研究了一种新的GPS/INS组合定姿自适应卡尔曼滤波方法,理论分析表明,该方法不但对姿态信息具有较好的估计性能,而且对系统模型的精确性、噪声特性具备良好的鲁棒性.最后,利用模拟数据对所研究算法进行了分析计算,与传统的卡尔曼滤波方法进行了比较、分析,结果表明所设计组合算法在精度、稳定性以及鲁棒性等方面较传统卡尔曼方法具有良好的特性.     

GPS navigation with increased immunity to modeling errors

A robust extended Kalman filter (EKF) is presented for GPS navigation with outlying errors in the GPS measurements due to failed satellites or unmodeled errors. In this approach, the innovation step of the conventional EKF is modified using the robust statistics concept to obtain a more accurate state estimate in the presence of outlying errors. Performance of the proposed robust EKF in comparison with the conventional EKF, and adaptive EKF is demonstrated through simulation results.     

改进的GPS/INS组合导航选星算法

在已有的 GPS定位的选星算法的基础上,从组合系统量测矩阵出发提出了一种适用于 GPS/INS组合导航的选星算法。此算法在原有算法的基础上,考虑了卫星仰角、方位角对卡尔曼滤波器可观性的影响,避免因观测量与系统状态间关联项的减少而导致的滤波器可观性下降,从而保证滤波器性能的稳定。     

Analysis of upgraded GPS internal Kalman filter

GPS receivers with provisions for inertial navigation system (INS) aiding are designed with internal Kalman filters that model generic INSs and process the basic GPS pseudorange and deltarange (range-rate) data to produce an output of inertially-smoothed, “GPS-derived” position and velocity. These Kalman filters model only the basic nine INS errors (position, velocity, and tilt) and do not model any INS gyro or accelerometer errors. It was found that a significant performance improvement could be achieved under conditions of degraded GPS satellite availability by augmenting this type of filter with the six INS gyro and accelerometer bias errors. It is, therefore, recommended that serious consideration be given to incorporating these states into the design of the GPS internal Kalman filter     

SINS/BDS紧组合系统中自适应 两阶段扩展卡尔曼滤波

在实际应用中，以伪距/伪距率为观测量的SINS/BDS紧组合导航系统，存在量测噪声的统计特性与实际不相符的情况，传统扩展卡尔曼滤波（EKF）方法无法有效解决这一问题，从而引起滤波误差增大。提出了一种SINS/BDS紧组合导航系统的GDOP估算及在线估计量测噪声的自适应两阶段EKF（ATEKF）方法，该方法使用经过紧组合修正后的SINS输出的位置，并结合星历数据中提供的卫星位置求解GDOP。在此基础上，利用GDOP值以及新息，实现了紧组合导航系统的量测噪声方差阵（Rk）的在线实时估计，从而达到自适应滤波的效果，改善导航精度。     

A relative navigation system for formation flight

A relative navigation system based on both the Inertial Navigation System (INS) and the Global Positioning System (GPS) is developed to support situational awareness during formation flight. The architecture of the system requires an INS/GPS integration across two aircraft via a data link. A fault-tolerant federated filter is used to estimate the relative INS errors based on relative GPS measurements and a range measurement obtained from the data link. The filter is constructed based on a reduced-order model of the relative INS error process. A method for analyzing the filter performance is presented. A case involving two helicopters in formation flight is studied under three different night trajectories to account for the effect of vehicle motion on the INS state transition matrix. The results of the covariance analysis are compared with actual night results over an instrumented test range.     

SINS／GPS／CNS组合导航中的鲁棒滤波算法研究

组合导航系统难以精确建模、噪声统计特性难以获得等问题逐渐突出,严重影响了组合导航系统滤波器的稳定性。为提高SINS／GPS／CNS组合导航系统的鲁棒性,设计了基于H∞滤波理论的鲁棒滤波算法,提高了导航系统对系统模型参数变化的适应能力。通过仿真与线性卡尔曼滤波器进行对比,验证了在系统模型参数变化的情况下,H∞滤波器精度几乎不发生改变,有更好的鲁棒性;提高了组合导航系统在噪声统计特性和模型参数不易确定情况下的导航性能。     

一种基于冗余测量的自适应卡尔曼滤波算法

针对目前自适应滤波算法的不足,在测量系统量测噪声方差未知的情况下,设计了一种基于冗余测量的自适应卡尔曼滤波(RMAKF)算法。通过对系统冗余测量值的一阶、二阶差分序列进行有效的统计分析,可以准确估计系统量测噪声统计特性,进而在滤波过程中自适应调节噪声方差阵R,提高滤波精度。以全球定位系统/惯性导航系统(GPS/INS)松组合导航系统为对象进行了仿真实验,结果表明该算法在测量系统噪声特性未知或发生改变时,可对其进行准确估计,在采用低精度惯性器件情况下,滤波结果较其他主要自适应卡尔曼滤波算法有较明显的改进。     

卫星导航接收机自主完好性监测算法研究

针对航空导航定位高可靠性的要求和GPS接收机观测噪声分布的特点,研究将粒子滤波算法应用于接收机自主完好性监测（RUM）中.通过粒子滤波算法对状态进行精确估计,利用对数似然比建立一致性检验统计量进行故障检测与隔离.对算法进行了数学建模,描述了完整的RAIM算法详细流程.通过实测数据对提出的RAIM算法进行验证,结果表明：粒子滤波算法在非高斯测量噪声情况下可以对GPS接收机状态进行精确的估计,利用对数似然比建立的一致性检验统计量能有效地检测并隔离故障卫星,验证了该算法应用于GPS接收机自主完好性监测的可行性和有效性.     

自适应扩展增量 Kalman 滤波方法

基于扩展增量Kalman滤波方法(EIKF)和自适应增量Kalman滤波(AIKF),建立自适应扩展增量Kalman(AEIKF)模型及其分析方法,给出递推算法.在许多实际情况(如深空探测),由于环境因素的影响、测量设备的不稳定性等原因,量测方程往往存在未知的系统误差,并且模型参数也具有不确定性,结果导致较大的Kalman滤波误差,影响滤波的收敛性.提出的AEIKF方法能够成功消除这种未知的系统误差,并能够实时估计变化的噪声统计量,提高Kalman滤波精度.该方法计算简单,便于工程应用.      

利用卡尔曼滤波求取直升机气动导数的应用研究

本文主要介绍利用低通滤波、卡尔曼滤波及最小二乘法求取直升机气动导数的方法。本方法的特点是,通过低通滤波使旋翼高频成份的影响减至最小,同时求取试验数据的测量噪声和过程噪声,然后通过卡尔曼滤波使试验数据包含的随机噪声减至最小,最后用最小二乘法求得直升机的气动导数,为了提高卡尔曼滤波的准确度,用最小二乘法由试验数据求取直升机的气动导数作为卡尔曼滤波时的初始导数。计算结果表明,该方法可使试验数据中包含的噪声大大减小,误差带减少70％以上,而计算工作量又远远小于最大似然法。     

Additional results on `reducing geometric dilution of precisionusing ridge regression'

R.J. Kelly (1990) presented preliminary results on the feasibility of using ridge regression to reduce the effects of geometric dilution of precision (GDOP) error inflation in position-fix navigation systems. Results indicate that the ridge technique will not reduce bias inflation due to the effects of GDOP in applications where bias-like measurement errors persist for time periods which greatly exceed the response time of an aircraft's guidance loop. This conclusion will preclude the use of ridge regression on navigation systems whose dominant error sources are bias-like. This applies in particular to the Global Positioning System (GPS) selective availability error source. All the simulation results given in Kelly's work are, however, valid for the conditions defined. Although ridge regression has not yielded a satisfactory solution to the general GDOP problem, it has illuminated the role that multicollinearity plays in navigation signal processors such as the Kalman filter. After a background discussion, bias inflation, initial position guess errors, ridge parameter selection methodology, and the recursive ridge filter, are discussed     

卡尔曼滤波在自旋卫星章动控制中的应用

细长体结构卫星的自旋运动是不稳定的,章动有发散趋势,须进行实时的章动监视,且章动角的测量值包含有误差.通过引入一个离散控制过程的系统,介绍了卡尔曼滤波器的基本原理,建立了自旋卫星自旋运动简化的动力学模型,给出了运动方程和观测方程,详细地推导了适用于自旋卫星章动控制的卡尔曼滤波处理过程,利用该模型对某自旋卫星章动进行仿真计算,并对姿态章动联合控制和应急章动控制情况下的章动控制应用情况进行分析,得出结论：基于卡尔曼滤波的卫星章动角计算的方法正确、结果准确.     

高超声速飞行器组合导航鲁棒滤波算法

高超声速飞行器由于其飞行环境的影响,使得GPS和星敏感器的量测噪声表现出非高斯特性。针对常规基于Kalman滤波的组合导航在非高斯噪声下性能下降的问题,提出了基于鲁棒滤波的高超声速飞行器组合导航算法。方法在惯性／GPS／＆异步量测建模的基础上,通过随机去耦将量测更新转化为线性回归问题,并基于M估计获得状态量最优估计。仿真结果表明,方法对非高斯噪声具有更好的鲁棒性,有效提高了高超声速飞行器组合导航系统的性能。     

利用GPS进行姿态估计的一种算法

首先建立了全球定位系统（ＧＰＳ）姿态确定的观测方程;然后给出了利用ＧＰＳ进行飞行器姿态估计的模型,并对该模型进行了线性处理;最后利用攻推广卡尔曼滤波技术,针对某飞行器进行了仿真计算。计算结果表明,对于不同的测量噪声和系统噪声,滤波器都有较好的估计,姿态估计的精度明显高于单纯ＧＰＳ姿态确定的精度,可以满足大多数飞行器对姿态确定的要求,证实了模型和算法可用性。     

SNR-based multipath error correction for GPS differential phase

Carrier phase multipath is currently the limiting error source for high precision Global Positioning System (GPS) applications such as attitude determination and short baseline surveying. Multipath is the corruption of the direct GPS signal by one or more signals reflected from the local surroundings. Multipath reflections affect both the carrier phase measured by the receiver and signal-to-noise ratio (SNR). A technique is described which uses the SNR information to correct multipath errors in differential phase observations. The potential of the technique to reduce multipath to almost the level of receiver noise was demonstrated in simulations. The effectiveness on real data was demonstrated with controlled static experiments. Small errors remained, predominantly from high frequency multipath. The low frequency multipath was virtually eliminated. The remaining high frequency receiver noise can be easily removed by smoothing or Kalman filtering     

Novel quaternion Kalman filter

This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quaternion kinematics equation is linear, the combination of the two yields a linear KF that eliminates the usual linearization procedure and is less sensitive to initial estimation errors. General accurate expressions for the covariance matrices of the system state-dependent noises are developed. In addition, an analysis shows how to compute these covariance matrices efficiently. An adaptive version of the filter is also developed to handle modeling errors of the dynamic system noise statistics. Monte-Carlo simulations are carried out that demonstrate the efficiency of both versions of the filter. In the particular case of high initial estimation errors, a typical extended Kalman filter (EKF) fails to converge whereas the proposed filter succeeds.     

GPS/INS组合导航系统自适应滤波算法与仿真研究

随着组合导航系统应用环境的日趋复杂，给噪声统计特性的准确描述带来困难，这将造成Kalman滤波器不稳定甚至发散。首先对目前解决此问题常用的自适应滤波方法进行了总结和分析，在此基础上，给出了基于滤波收敛性判据，结合Sage-Husa自适应滤波和强跟踪Kalman滤波的改进自适应滤波算法。最后以GPS／INS组合导航系统为例进行了计算机仿真，结果表明：该算法可有效抑制滤波发散，具有较大范围的自适应能力。