模糊自适应算法在GPS/INS组合导航系统中的应用

阐述了在对常用的Sage-husa自适应滤波算法总结和分析基础上,给出了滤波收敛的判据,提出利用模糊逻辑自适应控制器(FLAC)调整卡尔曼滤波器的方法,形成自适应滤波算法.以GPS/INS组合导航系统为例进行了仿真,结果既能抑制滤波发散,又能提高滤波精度和实时性.     

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。     

对GPS／INS制导巡航导弹GPS干扰方法的探讨

目前GPS/INS制导已成为精确制导武器的核心。本文根据GPS信号特点及GPS/INS制导机理,通过对压制干扰和欺骗干扰技术及其对GPS接收机影响的分析,着重探讨对GPS/INS制导巡航导弹GPS干扰的方法。若要提高对GPS/INS制导巡航导弹实施远距离干扰的效果,而又使干扰机功率不是很大,则需建立多层次、分布式、立体式、小功率GPS干扰体系。     

MIMU/GPS/磁强计组合导航系统序贯抗差自适应滤波器设计

将磁强计应用于INS/GPS组合导航系统中,其提供的航向角信息可以有效抑制惯导系统的误差积累并提高航向角的可观测性,但磁强计应用中一个突出的问题在于,除了罗差之外,它仍然容易受到环境中各种异常磁场的干扰。为解决这一问题,本文采用残差检验对磁场干扰进行检测,然后提出一种基于序贯处理的抗差自适应滤波算法,对实现对滤波结果的修正。基于跑车实验的离线数据分析表明,该滤波算法能有效抑制磁场干扰的影响,且具有较好的敏感性和鲁棒性。     

基于RTS滤波的GPS+BDS非差非组合PPP/INS紧组合模型

GPS高精度定位技术在动态复杂环境中,其定位精度、可靠性和连续性因卫星信号频繁失锁而变差。为此,提出了采用基于RTS滤波(Rauch-Tung-Striebel Filter)的GPS+BDS非差非组合PPP(Precise Point Positioning)与INS(Inertial Navigation System)紧组合模型的策略来克服GPS在动态定位中的弱点。其中,采用GPS+BDS双系统观测数据,可提高PPP解算中的可用卫星数,改善星站间定位几何强度和提高PPP收敛速度;采用PPP/INS紧组合,利用INS的自主定位特性和短期高精度特性,可有效改善复杂环境下的定位精度和连续性;采用RTS滤波,可进一步提高PPP/INS紧组合性能。首先推导了GPS+BDS非差非组合函数模型、PPP/INS紧组合函数模型和RTS滤波函数模型,然后利用一组车载动态数据,对动态GPS PPP、GPS+BDS PPP、GPS/INS紧组合、GPS+BDS PPP/INS紧组合和基于RTS的GPS+BDS PPP/IMU紧组合的定位、测速和定姿性能进行分析。实验结果表明,该方案可有效提高定位(58%～72%)、测速(74%～82%)和定姿(4%～23%)精度,特别是对卫星失锁期间的定位性能改善尤为明显。     

一种对GPS/INS组合导航的曳引式拉偏干扰

为解决GPS/INS组合导航对抗难题,提出一种针对GPS/INS组合导航的曳引式拉偏干扰方法。通过干扰设备产生欺骗干扰信号,使GPS/INS组合中的GPS接收机输出与其实际位置逐渐偏离的导航定位数据,当偏离误差无法被组合导航纠正时发生曳引式拉偏干扰。文中给出干扰方法的定义、信号形式、简化形式,通过半实物仿真实验证明其有效性并对其干扰效果进行了分析。     

INS辅助GPS接收机及抗干扰能力的分析

针对高动态或低信噪比条件下GPS卫星信号容易失锁的特点,根据GPS接收机码／载波跟踪环特性,研究分析了惯性导航（INS）辅助GPS接收机原理及其抗干扰能力。在复杂电磁环境下,INS辅助GPS接收机（特别是紧耦合GPS/INS组合模式）是组合导航的发展方向。     

改进的Sage-Husa自适应滤波算法在MEMS航姿参考系统中的应用

针对经典Kalman滤波和扩展Kalman滤波融合算法存在的计算量大、精度低、实时性差的缺点，引入了改进的Sage-Husa自适应扩展Kalman滤波算法。该算法对经典扩展Kalman滤波算法进行了自适应改进，并在此基础上利用加权渐消记忆法获取了遗忘因子，并通过预测残差得出了最优解。同时，用调整有偏增益估计的措施来保证系统噪声预测方差矩阵与噪声预测方差矩阵的对称性和正定性，对滤波器发散进行了有效的抑制，减少了算法的计算量。实验结果表明，该算法有效改善了可靠性、精确性及自适应能力。     

Discussion for Deeply-coupled GPS/INS Technology

面对未来信息化、电子化战争复杂的电磁环境和高速、超高速精确制导武器发展需求,传统GPS/INS组合导航技术很难满足武器性能指标的要求.而GPS/INS深组合导航系统能在高动态和复杂电磁环境下为精确制导武器提供稳定、可靠、精确的导航信息,实现精确制导武器在复杂战场环境下对目标的精确打击.因此GPS/INS深组合技术成为近来人们的研究热点.深组合技术除了传统意义上利用GPS接收机信息修正INS之外,同时,采用INS导航数据还能对GPS接收机载波跟踪环路进行外部辅助,剔除信号中的动态信息,减小GPS接收机载波跟踪环对信号的跟踪范围,压缩环路带宽,提高接收机在高动态环境下工作稳定性和接收机抗干扰性能,以保证组合导航系统的可用性和可靠性.     

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.      

Multipath-adaptive GPS/INS receiver

Multipath interference is one of the contributing sources of errors in precise global positioning system (GPS) position determination. This paper identifies key parameters of a multipath signal, focusing on estimating them accurately in order to mitigate multipath effects. Multiple model adaptive estimation (MMAE) techniques are applied to an inertial navigation system (INS)-coupled GPS receiver, based on a federated (distributed) Kalman filter design, to estimate the desired multipath parameters. The system configuration is one in which a GPS receiver and an INS are integrated together at the level of the in-phase and quadrature phase (I and Q) signals, rather than at the level of pseudo-range signals or navigation solutions. The system model of the MMAE is presented and the elemental Kalman filter design is examined. Different parameter search spaces are examined for accurate multipath parameter identification. The resulting GPS/INS receiver designs are validated through computer simulation of a user receiving signals from GPS satellites with multipath signal interference present The designed adaptive receiver provides pseudo-range estimates that are corrected for the effects of multipath interference, resulting in an integrated system that performs well with or without multipath interference present.     

Design and Development of a Real-Time DSP and FPGA-Based Integrated GPS-INS System for Compact and Low Power Applications

Emphasis of the present work is on an elegant real-time solution for GPS/INS integration. Micro-electro mechanical system (MEMS) based inertial sensors are light but not accurate enough for inertial navigation system (INS) applications. An integrated INS/GPS system provides better accuracy compared with either INS or GPS, used individually. This paper describes an improved design and fabrication of a loosely coupled INS-GPS integrated system. The systems currently available use commercial off-the-shelf (COTS) hardware and are, therefore, not optimized for compact, single supply, and low power requirements. In the proposed system, a digital signal processor (DSP) is used for inertial navigation solution and Kalman filter computations. A field programmable gate array (FPGA) is used for creating an efficient interface of the GPS with the DSP. Direct serial interface of the GPS involve tedious processing overhead on the navigation processor. Therefore, a universal asynchronous receiver transmitter (UART) and dual port random axis memory (DPRAM) are created on the FPGA itself. This also reduces the total chip count, resulting in a compact system. The system is designed to give real time processed navigation solutions with an update rate of 100 Hz. All the details of this work are presented.     

基于置信度加权的组合导航数据融合算法

 针对联邦滤波融合算法中由于模型量测噪声统计特性未能被准确描述导致其子滤波器误差变大，进而导致联邦滤波估计出现偏差的问题，为了改进联邦滤波融合方法，将模糊自适应卡尔曼滤波方法和置信度加权方法与联邦滤波融合方法相结合，应用于组合导航系统。该方法首先将模糊自适应卡尔曼滤波方法应用于各子滤波器，使其能够跟踪真实量测噪声统计特性。然后通过模糊方法计算得到各子滤波器的置信度，进而得到联邦滤波器的置信度，再由得到的置信度对各子滤波器及联邦滤波器输出进行加权，得到最终的全局输出。对车载组合导航系统的仿真结果表明，这种算法对量测噪声具有较强的自适应性，能够抑制置信度低的子滤波器在融合系统中所占的权重，提高联邦滤波融合算法的精度，是一种可行的车载组合导航数据融合算法。     

GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update

An Extended Kalman Filter (EKF) is commonly used to fuse raw Global Navigation Satellite System (GNSS) measurements and Inertial Navigation System (INS) derived measurements. However, the Conventional EKF (CEKF) suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance. In this research, an Adaptive Interacting Multiple Model (AIMM) filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model (IMM) algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) were post-processed in differential mode. The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpart for GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.     

车载组合导航中的自适应滤波技术研究

针对现有的自适应卡尔曼滤波算法结构繁杂,采用了一种自适应卡尔曼滤波的车载SINS/GPS 组合导航算法,并与常规卡尔曼滤波算法作了比较。仿真结果表明,这种算法具有结构简单、实时性好和抗野值等的优点,不失为一种实用而有效的滤波方法。     

Enhancing filter robustness in cascaded GPS-INS integrations

Filter robustness is defined herein as the ability of the Global Positioning System/Inertial Navigation System (GPS-INS) Kalman filter to cope with adverse environments and input conditions, to successfully identify such conditions and to take evasive action. The formulation of two such techniques for a cascaded GPS-INS Kalman filter integration is discussed This is an integration in which the navigation solution from a GPS receiver is used as a measurement in the filter to estimate inertial errors and instrument biases. The first technique presented discusses the handling of GPS position biases. These are due to errors in the GPS satellite segment, and are known to be unobservable. They change levels when a satellite constellation change occurs, at which point they introduce undesirable filter response transients. A method of suppressing these transients is presented. The second technique presented deals with the proper identification of the filter measurement noise. Successful formulation of the noise statistics is a factor vital to the healthy estimation of the filter gains and operation. Furthermore, confidence in the formulation of these statistics can lead to the proper screening and rejection of bad data in the filter. A method of formulating the filter noise statistics dynamically based on inputs from the GPS and the INS is discussed     

基于强跟踪滤波的GPS/INS组合导航系统对准技术研究

针对卡尔曼滤波鲁棒性较差的问题,研究了基于强跟踪滤波方法的GPS/INS对准。建立了GPS/INS组合导航系统对准的误差模型,对机载装备系统进行GPS/INS组合导航系统的对准仿真分析,验证该方案的可行性及强跟踪滤波器的性能。仿真结果表明,采用强跟踪滤波能够根据残差的变化求出渐消因子,能够在机动过程中有效跟踪系统状态量,从而提高对准精度和速度。采用强跟踪滤波的GPS/INS组合导航系统对准技术可以保证对准的快速性及对准精度,对工程应用具有重要的参考价值。     

一种基于可观测度分析的SINS/GPS自适应-反馈校正滤波新方法

 针对机载捷联惯导系统(SINS)/全球定位系统（GPS）组合导航系统不完全可观测导致滤波器精度下降甚至发散的问题,提出了一种基于系统状态可观测度分析的自适应反馈校正滤波新方法。该滤波方法改进了系统可观测度的归一化处理方法,将归一化处理后的系统状态可观测度作为反馈因子,对SINS系统进行自适应反馈校正。最后,将该方法应用于机载合成孔径雷达(SAR)运动补偿用SINS/GPS组合导航系统中,飞行试验结果表明该方法在系统不完全可观测的情况下有效地提高了导航精度。     

基于联邦滤波结构的INS／GPS组合导航系统数据融合研究

为了研究平台式惯导INS（interial navigation system）和全球定位系统GPS（globe position system）组合导航联邦滤波器的实现,使用速度局部滤波器和位置局部滤波器,分别对INS／GPS组合导航系统的向东速度、向北速度,以及对经度和纬度进行卡尔曼滤波,然后将位置数据和速度数据输入主滤波进行数据融合。以无人机的向东匀速水平飞行为背景,运用联邦卡尔曼滤波器算法,使用matelab进行仿真分析。可以证明联邦滤波器算法简单,易于实现,并且可以提高导航系统精度．实际应用中此方法可行。