GPS/DR组合导航系统自适应扩展卡尔曼滤波模型的建立*

建立了车载ＧＰＳ／ＤＲ组合导航系统非线性自适应卡尔曼滤波模型及算法，首次提出了依据ＰＤＯＰ等ＧＰＳ定位的输出参数，自动调整Ｒ，Ｑ的大小，从而自地调整组合导航系统模型性能的方法，使得模型能够适应各种情况，具有“智能”模型的性质，计算机仿真表明应用该模型具有良好效果。     

DR/GPS组合导航非线性自适应信息融合算法研究

针对实际工程中组合导航系统采集数据融合处理问题,建立了系统实测数学模型,提出了基于非线性系统的自适应信息融合算法,将系统未建模部分及高阶项作为噪声项与状态向量进行耦合估计,改善滤波算法对模型误差以及噪声假设的敏感性,通过仿真对比验证算法改进前后的效果,并用于DR/GPS组合导航系统。实验结果表明提出的数据融合算法能够提高系统的精确性,具有一定的实用价值。     

GNSS/DR组合导航系统中粒子滤波算法研究

随着车辆组合导航系统的发展,其算法研究也引起了广泛的重视。该文对车载GPS/DR组合导航系统的算法进行了研究,由于卫星信号易受到复杂环境的干扰和影响,导致使用卡尔曼滤波会有较大的误差结果。粒子滤波就能很好的处理这种情况,具有鲁棒性。仿真结果表明,粒子滤波算法优于卡尔曼滤波,更能减少定位误差。     

神经网络辅助的GPS/INS组合导航滤波算法研究

在高空高速条件下,GPS信号失锁致使常规的卡尔曼滤波器发散,从而导致组合导航系统精度严重下降。以BP神经网络辅助技术手段对GPS/INS组合导航滤波算法实施精度补偿,即在GPS信号锁定时,对神经网络进行实时在线训练,而当在GPS信号失锁时,利用之前训练好的神经网络进行组合导航滤波,以解决精度严重下降问题。算法采用多神经网络并行结构,以减少神经网络在训练过程中的交叉耦合,提高训练速度。通过MATLAB仿真,验证了算法的可靠性与可行性,并证明其在GPS信号丢失时,精度较纯惯性导航系统有较大提高。     

基于M估计的非线性鲁棒检测卡尔曼滤波算法

针对传统鲁棒非线性滤波在观测噪声为非高斯强干扰噪声情况下,滤波性能下降的问题,提出一种利用卡方检测法预判断的非线性鲁棒检测滤波算法。该算法通过卡方检测设置门限,剔除突变野值,利用M估计修正量测更新。仿真实验对比了几种典型非线性滤波方法在不同观测噪声环境下的性能。所提算法在非高斯强干扰噪声情况下,比传统鲁棒滤波算法估计精度平均提高了25.5%;估计方差平均减少了18.3%。实验结果表明:所提算法可以抑制观测量非高斯强干扰噪声的影响,提高滤波精度及稳定性。     

基于ANFIS神经网络的GPS/INS组合导航信息融合

介绍了自适应神经网络模糊推理技术(ANFIS),在此基础上采取新息自适应调整的思想,设计了一种基于滤波器工作参数调整的GPS/INS组合导航神经网络辅助卡尔曼滤波器,利用神经网络的非线性,根据滤波器的实际输出在线实时动态调整滤波器参数,达到对滤波器的调整和控制。与传统卡尔曼滤波器进行计算机仿真比较表明,基于ANFIS神经网络的GPS/INS组合导航信息融合技术具有较强的自适应性,能够在复杂的环境下抑制数据的发散,提高导航精度。     

自适应卡尔曼滤波在组合导航中的应用研究

对惯性导航系统（INS）与全球导航系统（GPS）分别进行了具体探讨,对比了两者的优缺点,针对INS／GPS组合导航系统中由于模型不准或因量测噪声的复杂多变造成的发散问题,引入了一种基于输出相关法的自适应卡尔曼滤波技术。通过在自适应滤波算法中推算最优稳态增益来调整量测噪声,抑制滤波器的发散,为GPS／INS组合导航系统实现高精度导航提供了有效的途径。仿真结果表明该算法能很好地对系统状态进行最优估计并适应系统噪声的变化,具有比常规卡尔曼滤波更高的导航精度。     

基于MIMU/GPS组合导航系统的应用研究

根据车载导航系统发展现状,提出了一种基于GPS和MIMU技术的低成本的、可用于市场推广的组合导航系统方案。采用MIMU与GPS松散组合方式,以速度和位置作为观测量设计了Kalman滤波器。该方案以ARM9作为中央处理器,详细介绍了系统的设计与实现。经测试,该系统与传统GPS相比,具有更高的稳定性和更精确的导航精度。     

星载SINS/GPS自主组合导航系统仿真研究

重点讨论了应用于低轨道卫星的自主组合导航滤波器设计问题。考虑星上环境、精度要求和可靠性,对标准卡尔曼滤波器进行了改进。采用加权衰减记忆滤波抑制滤波发散,同时设计简易的最小二乘器实时监控卡尔曼滤渡过程是否发散,若发散则重置,进行分段滤波。通过仿真,证实了该方法的可行性,且滤波效果较理想、精度较高。     

SINS/GPS组合导航系统Kalman滤波仿真研究

为提高捷联惯导系统SINS和全球定位系统GPS的精度和可靠性,研究了SINS和GPS的原理,建立了SINS/GPS系统的状态方程和位置速度误差量测方程;并采用卡尔曼滤波算法实现了SINS/GPS的组合导航.Matlab仿真结果证明,采用Kalman滤波实现SINS/GPS组合导航,其精度得到大大提高;且采用SINS/GPS组合导航系统,克服了SINS惯性导航难以长时间独立工作的缺点,解决了GPS易失锁、难以实时控制的不足,保证了导航系统的实时性及较高的精度和可靠性.     

Training Recurrent Neurocontrollers for Robustness With Derivative-Free Kalman Filter

We are interested in training neurocontrollers for robustness on discrete-time models of physical systems. Our neurocontrollers are implemented as recurrent neural networks (RNNs). A model of the system to be controlled is known to the extent of parameters and/or signal uncertainties. Parameter values are drawn from a known distribution. For each instance of the model with specified parameters, a recurrent neurocontroller is trained by evaluating sensitivities of the model outputs to perturbations of the neurocontroller weights and incrementally updating the weights. Our training process strives to minimize a quadratic cost function averaged over many different models. In the end, the process yields a robust recurrent neurocontroller, which is ready for deployment with fixed weights. We employ a derivative-free Kalman filter algorithm proposed by Norgaard and extended by Feldkamp (2001) and Feldkamp (2002) to neural network training. Our training algorithm combines effectiveness of a second-order training method with universal applicability to both differentiable and nondifferentiable systems. Our approach is that of model reference control, and it extends significantly the capabilities proposed by Prokhorov (2001). We illustrate it with two examples     

Applying the unscented Kalman filter for nonlinear state estimation

Based on presentation of the principles of the EKF and UKF for state estimation, we discuss the differences of the two approaches. Four rather different simulation cases are considered to compare the performance. A simple procedure to include state constraints in the UKF is proposed and tested. The overall impression is that the performance of the UKF is better than the EKF in terms of robustness and speed of convergence. The computational load in applying the UKF is comparable to the EKF.     

自适应GPS扩展卡尔曼定位算法研究

针对全球定位系统(GPS)信号定位过程中存在多径导致定位误差,尤其静态环境中零频差短多径引发的定位拖尾现象,提出了一种自适应估计多径残留的扩展卡尔曼滤波算法,实现了静态环境中零频差短多径抑制。首先量化地给出了基带多径抑制后的多径残留模型,即多径呈现"矩形"类型分布,以此为基础设计了一种自适应估计多径残留的方法,即在拟合窗口内估计伪距测量误差的均值和标准差,作为EKF算法的测量误差协方差矩阵,实现了EKF中多径的动态估计。最后通过仿真表明,本文的自适应估计多径残留的扩展卡尔曼滤波(ARKF)能有效抑制零频差短多径影响。     

An alternative Kalman innovation filter approach for receiver position estimation based on GPS measurements

This article presents an alternative Kalman innovation filter approach for receiver position estimation, based on pseudorange measurements of the global positioning system. First, a dynamic pseudorange model is represented as an ARMAX model and a pseudorange state-space innovation model suitable for both parameter identification and state estimation. The Kalman gain in the pseudorange coordinates is directly calculated from the identified parameters without prior knowledge of the noise properties and the receiver parameters. Then, the pseudorange state-space innovation model is transformed into the receiver state-space innovation model for optimal estimation of the receiver position. Hence, the proposed approach overcomes the drawbacks of the classical Kalman filter approach since it does not require prior knowledge of the noise properties, and the receiver's dynamic model to calculate the Kalman gain. In addition, due to its simplicity, it can be easily implemented in any receiver. To demonstrate the effectiveness of the approach, it is utilized to estimate the position of a stationary receiver and its performance is compared against two versions of the classical Kalman filter approach. The results show that the proposed approach yields consistently good estimation of the receiver position and outperforms the other methods.     

汽油机非线性卡尔曼滤波暂态进气量估计及空燃比控制

暂态工况下缸进气量的准确估计是提高发动机空燃比控制精度的有效措施之一,为此本文提出一种基于无迹卡尔曼滤波的暂态缸进气量估计算法,并利用估计的缸进气量设计了一种前馈-反馈空燃比控制器.MATLAB环境下的仿真实验给出了所提出的算法与现有进气量估计算法的比较,同时基于暂态气量估计的空燃比控制仿真实验验证了估计的有效性.论文与现有成果的区别在于:一是暂态进气量估计模型不仅包含了歧管压力动态还考虑了曲轴角速度动态,并采用了基于非线性辨识的均值模型;二是考虑了泵气波动的影响,采用了移动平均值法的数字滤波器对泵气波动进行滤波;三是采用无迹卡尔曼滤波算法对歧管压力和曲轴角速度进行估计.     

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.     

LPV模型的Kalman滤波器设计

针对线性变参数（LPV）模型,给出了一种改进Kalman滤波器设计方法,这种滤波方法可以根据系统LPV模型实时调整滤波状态方程矩阵系数,从而增强滤波算法对系统模型变化的跟踪性能,并提高滤波准确性。结合具体实例,通过仿真比较,较好地检验了该方法的有效性。     

Multiconfiguration Kalman filter design for high-performance GPS navigation

This paper describes the design, implementation, and performance of a real-time multiconfiguration Kalman filter for high-performance Navstar global positioning system (GPS) navigation. The design provides extreme flexibility in order to operate with a wide variety of host sensors. It configures automatically (four filter configurations) based upon the host vehicle requirements and sensor availability, in order to process GPS measurements and provide the best estimate of the navigation states. Two new techniques, namely an unaided dead-reckoning Kalman filter implementation and an automatic inertial platform tilt estimation control scheme, are developed to improve the navigation accuracy, especially for high-dynamics applications. Performance results are presented to demonstrate the advantages of these techniques.     

非线性自适应平方根无迹卡尔曼滤波方法研究

针对带有附加噪声且噪声特性未知的系统,提出了一种非线性卡尔曼滤波方法--自适应平方根无迹卡尔曼滤波（NASRUKF）方法,该方法基于平方根滤波的思想,对传统的Sage-Husa自适应滤波算法进行了改进,并与平方根无迹卡尔曼滤波（SRUKF）算法相结合用来进行非线性滤波。该算法能直接对非线性系统的状态方差阵和噪声方差阵的平方根进行递推与估算,确保状态和噪声方差阵的对称性和非负定性。将所提方法通过计算机仿真技术与SRUKF算法进行对比,结果表明NASRUKF方法在滤波精度、稳定性和自适应能力方面均优于SRUKF方法。