Adaptive Federal Kalman Filtering for SINS／GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation‘s error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.     

Bridging GPS outages of tightly-coupled GPS/SINS using GMDH neural network

A tightly-coupled GPS(global positioning system)/SINS(strap-down inertial navigation system)based on a GMDH(group method of data handling)neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality.Positions and velocities of the satellites were predicted by a GMDH neural network,and the pseudo-ranges and pseudo-range rates received by the GPS receiver were simulated to ensure the regular operation of the GPS/SINS Kalman filter during outages.In the mathematical simulation a tightly-coupled navigation system with a proposed approach has better navigation accuracy during GPS outages,and the anti-jamming ability is strengthened for the tightly-coupled navigation system.     

SINS/GPS组合导航方法设计

根据SINS/GPS组合导航的基本原理,重点研究联合卡尔曼滤波方法在组合导航中的应用,基于最优化理论的数据融合技术,通过分析纯惯性导航系统的误差,建立了组合导航系统的误差模型,设计了高可靠性、易于工程实现的简化联合卡尔曼滤波器和对应的数据融合算法。并对该导航系统进行了动态实验,结果表明组合导航能有效抑制导航参数误差,提高导航精度。     

车载组合导航的自适应滤波

针对车载组合导航信息融合的高精度、高可靠性等要求,提出了一种组合导航的自适应集中滤波算法.该算法的主要思想是:以判别观测数据中的野值存在与否为算法切换条件,存在野值时采用改进的增益矩阵滤波处理方法,不存在野值时则采用模糊自适应集中滤波方法.将此方法用于SINS/GPS车载组合导航系统,实验表明,采用的这种自适应滤波方法,能够有效抑制滤波发散,滤波精度和收敛速度优于常规集中滤波,是一种有效的车载组合导航算法.     

Modified Observation Model in Tightly-Coupled INS/GPS Integration

The conventional Kalman filter (CKF) is widely used in tightly-coupled INS/GPS integrated navigation systems. The linearization accuracy of the CKF observation model is one of the decisive factors of the estimation accuracy and therefore navigation accuracy. Additionally, the conventional observation model (COM) used by the filter may be divergent, which would result into some terrible accuracies of INS/GPS integration navigation in some cases. To improve the navigation accuracy, the linearization accuracy of the COM still needs further improvement. To deal with this issue, the observation model is modified with the linearization of the range and range rate equations in this paper. Compared with COM, the modified observation model (MOM) further considers the difference between the real user position and the position calculated by SINS. To verify the advantages of this model, INS/GPS integrated navigation simulation experiments are conducted with the usage of COM and MOM respectively. According to the simulation results, the positions (velocities) calculated using COM are divergent over time while the others using MOM are convergent, which demonstrates the higher linearization accuracy of MOM.     

GPS/SINS组合导航系统在飞航导弹中的应用

研究了ＧＰＳ／ＳＩＮＳ组合导航系统在飞航导弹制导中的应用。文中着重研究位置速度组合模式的ＧＰＳ／ＳＩＮＳ组合导航系统的构成和算法，简化误差模型，引入衰减记忆滤波。仿真结果表明，组合导航系统较准确地给出导弹的位置、速度和姿态信息，大大提高了制导精度     

基于离散平稳小波变换的EKF数据融合算法

针对车辆GPS/DR组合导航系统中广泛采用的扩展卡尔曼滤波器进行数据融合难以建立精确的数学模型的问题,提出了基于离散平稳小波变换的扩展卡尔曼滤波器数据融合算法。仿真表明,该算法优于扩展卡尔曼滤波器,对车辆组合导航系统的数据融合是有效的。     

GPS/罗兰C/SINS/AHRS组合导航系统及试验

组合导航系统已成为现代船用导航系统的重要配套设备,并已向着多组合导航系统方向发展。针对导航精度及可靠性,设计了GPS/罗兰C/SINS/AHRS组合导航系统,该组合导航采用了自适应联邦卡尔曼滤波(Kalman)的方法,通过闭环形式校正SINS,并用姿态航向参考系统(AHRS)代替捷联惯性导航系统(SINS)输出的航向角以保证航向角的收敛,同时对罗兰C的测量值进行坐标变换以消除由于坐标系不同而带来的常值误差。实际的静态与动态试验结果表明,该组合导航系统是可行的,能满足任务要求,并且易于工程实现。     

组合导航系统的卡尔曼滤波器的设计

将多传感器数据融合的卡尔曼滤波器设计方法,运用在基于GPS／INS的微型飞行器的组合导航系统中,经理论分析及实际结果表明：该滤波器设计合理,算法具有全局最优,能够提高系统的容错能力及系统的导航精度。     

Federated unscented particle filtering algorithm for SINS/CNS/GPS system

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10⁻⁶ rad, 0.667×10⁻⁶ rad, 4.25 m) of FUKF to (0.403×10⁻⁶ rad, 0.251×10⁻⁶ rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.     

神经网络辅助的组合导航系统信息融合方案

传统的Kalman滤波器自适应能力弱，而单纯的神经网络滤波器估计精度较差，且网络训练经验性太强。面向组合导航领域，提出BP神经网络辅助自适应联邦Kalman滤波器方案，设计并实现了SINS／GPS／TAN／SAR智能化容错组合导航系统。结合自适应滤波和神经网络两种方法共同提高系统的自适应能力，并提出新的神经网络输入量，改善了算法的实时性。系统的估计精度得到显著提高，仿真结果证明了该方案的可行性和有效性。     

联邦卡尔曼滤波器在无人机导航系统中的应用

针对无人机GPS／RP／DR组合导航系统的特点，设计了用于该系统的联邦卡尔曼滤波器。该滤波器具有全局最优性，其结构遵循信息分配原则，其算法改善了数值计算的稳定性和系统的容错性，并减少了信息传输量与计算量。理论分析及仿真结果表明，该联邦卡尔曼滤波器能够满足无人机在GPS或RP信号无效或两信号均短期无效情况下定位导航的精度要求。     

GPS/INS动态卡尔曼滤波优化算法

GPS/INS组合导航系统动态定位数据的随机误差消除的重要方法是卡尔曼滤波,但运用扩展卡尔曼滤波器进行动态定位滤波时,需要对系统模型和观测模型以及误差模型进行准确建模,特别是载体状态机动时滤波器跟踪能力不强。提出一种GPS/INS组合导航动态卡尔曼滤波的优化算法,引入遗忘因子限制卡尔曼滤波器的记忆长度,充分利用现时的测量数据,改善滤波器的动态性能,并进行计算机仿真实验。仿真结果表明,遗忘因子增加,滤波器的跟踪能力增强,使滤波器达最佳性能。该优化算法比普通的扩展卡尔曼滤波算法的动态跟踪性能好,从而可显著提高导航系统定位精度。     

一种智能化的组合导航方法

利用不同导航系统的工作特点，用反馈-融合式联邦滤波方法，对SINS／GPS／TAN／LORAN-C进行组合，并利用模糊控制理论，对各导航系统的导航效果实行模糊判决，建立了模糊控制器，以实现组合导航系统的智能化管理，提高导航的可靠性。     

GPS／SINS超紧组合导航系统自适应混合滤波算法

针对传统反馈校正滤波结构中,由于不可观测状态的反馈导致系统滤波精度下降,以及由于全球定位系统／捷联惯性导航系统(GPS／SINS,global positioning system／strapdown inertial navigation system)超紧组合导航系统量测方程的非线性导致滤波难度的增加等问题,本文重新推导了线性的量测方程,并将基于状态可观测性的混合校正滤波算法应用于该模型．通过对比三种主流可观测性分析方法,选用误差协方差阵的特征值和特征向量可观测性分析方法分析系统状态的可观测性．最后根据可观测性分析的结果制定自适应的反馈因子,从而对SINS和GPS接收机误差进行校正．仿真结果显示,该方法可以有效提高不完全可观测系统的估计精度．     

基于ARM和DSP的嵌入式导航计算机方案设计

针对微小型组合导航、制导与控制系统对其导航计算机处理能力、体积、功耗及适用性等方面的要求，设计一种嵌入式高性能导航计算机系统.采用ARM芯片AT91RM9200和DSP芯片ADSP21364双组合处理器方案，进行外围电路的扩展，能够高效的完成信息采集、实时导航解算、卡尔曼滤波及发送控制指令任务.设计注重了导航计算机系统的可扩展性，可应用于多种微小型GPS/SINS，DGPS/SINS/MC等组合导航、制导与控制系统.较之传统的导航计算机，突出了体积小，功耗低及适用性强等特点.     

一种低成本GPS组合导航定位系统

提出一种低成本、简单实用的GPS组合定位系统,即采用电子计步器与电子罗盘辅助GPS.该系统可以工作在自主导航、纯GPS导航、组合导航3种模式.文中给出了组合原理、方法和基本算法,探讨了几种应用模式和可能的应用领域.     

基于ARM和DSP的嵌入式导航计算机方案设计

针对微小型组合导航、制导与控制系统对其导航计算机处理能力、体积、功耗及适用性等方面的要求，设计一种嵌入式高性能导航计算机系统．采用ARM芯片AT91RM9200和DSP芯片ADSP21364双组合处理器方案，进行外围电路的扩展，能够高效的完成信息采集、实时导航解算、卡尔曼滤波及发送控制指令任务．设计注重了导航计算机系统的可扩展性，可应用于多种微小型GPS／SINS，DGPS／SINS／MC等组合导航、制导与控制系统．较之传统的导航计算机，突出了体积小，功耗低及适用性强等特点．     

低成本MIMU/GPS组合导航系统设计与实验

高精度惯性传感器的昂贵价格在一定程度上限制了惯性导航系统(INS)的进一步推广应用,然而低成本微型惯性测量单元(MIMU)的出现,为INS的推广应用了带来了新的契机。文中提出了一种适合于工程应用的低成本MIMU/GPS组合导航系统的结构与方案,并给出了组合导航系统位置速度交替组合模式的数学模型。考虑到未知工作环境中各类噪声统计参数的未确定性易使经典的卡尔曼滤波器发散,因此文中采用了闭环自适应卡尔曼滤波器对子系统的数据进行融合处理;设计了低成本的组合导航系统原理样机,并进行了跑车实验。跑车实验及事后数据处理分析结果表明:该系统具有较高的长时间导航精度以及良好的可靠性和鲁棒性。     

无人水下航行器SINS/DVL组合导航方法研究

给出了应用于无人水下航行器远程航行的基于SINS/DVL的组合导航方法。利用Kalman滤波算法融合DVL测量的绝对速度估计SINS的导航参数误差,并进行校正。仿真结果说明这种方法能够有效提高导航系统精度,从仿真曲线还得到了闭合航路能够大幅度消除导航误差的结论。