A New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle

The extended particle filter (EPF) assisted by the Takagi-Sugeno (T-S) fuzzy logic adaptive system (FLAS) is used to design the ultra-tightly coupled GPS/INS (inertial navigation system) integrated navigation, which can maneuver the vehicle environment and the GPS outages scenario. The traditional integrated navigation designs adopt a loosely or tightly coupled architecture, for which the GPS receiver may lose the lock due to the interference/jamming scenarios, high dynamic environments, and the periods of partial GPS shading. An ultra-tight GPS/INS architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The EPF is a particle filter (PF) which uses the extended Kalman filter (EKF) to generate the proposal distribution. The PF depends mostly on the number of particles in order to achieve a better performance during the high dynamic environments and GPS outages. The T-S FLAS is one of these approaches that can prevent the divergence problem of the filter when the precise knowledge on the system models is not available. The results show that the proposed fuzzy adaptive EPF (FAEPF) can effectively improve the navigation estimation accuracy and reduce the computational load as compared with the EPF and the unscented Kalman filter (UKF).     

基于模糊逻辑的两步卡尔曼滤波器设计

在对惯性运动跟踪系统的建模分析中,常采用基于计算机的集中式卡尔曼滤波算法进行数据处理。由于该方法存在算法复杂,处理数据速度慢等问题,难以在嵌入式系统中实现高速运动跟踪,提出一种基于模糊逻辑的自适应两步卡尔曼滤波算法。该方法根据人体不同的运动状态调整卡尔曼滤波器,实验结果证明所提的方法能够更好地估计各个传感器的测量精度,减少了运算量,并在一定程度上提高了滤波器的容错性能。     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.     

Robust positioning technique in low-cost DR/GPS for land navigation

This paper describes a dead-reckoning (DR) construction for land navigation and sigma-point-based receding-horizon Kalman finite-impulse response (SPRHKF) filter for DR/GPS integration system. A simple DR construction is adopted to improve the performance of both pure land DR navigation and DR/GPS integration system. In order to overcome the flaws of the extended Kalman filter (EKF), the sigma-point KF (SPKF) is merged with the receding-horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can occur in the microelectromechanical systems' inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS integration system for land navigation seamlessly.     

改进的UKF在SINS/GPS/地磁组合导航中的应用

针对SINS(Strapdown Inertial Navigation System)/GPS组合导航系统中出现的滤波发散问题,研究自适应渐消卡尔曼滤波对于滤波发散的抑制作用,引入了一种新的渐消矩阵计算方法.为了提高导航精度,增加了地磁量,作为观测量对运载体的姿态、速度、位置进行校正,有效地解决了SINS初始状态的导航精度下降问题.车载实验证明,该算法简单,容易实现,能够有效抑制滤波发散,满足组合导航的精度要求.     

基于神经网络辅助观测的连续组合导航算法研究

针对因全球定位系统(GPS)信号失效导致捷联式组合导航系统SINS/GPS组合导航系统发散的问题,设计了一种基于神经网络辅助观测的智能组合导航算法.该方法在GPS信号有效时训练神经网络,当GPS失效后利用神经网络自主重构组合导航系统,将神经网络的输出信息作为观测量构建新的Kalman滤波器,以实现对捷联惯性导航系统误差的连续反馈校正,从而实现了高精度的连续导航.该方法得到了仿真验证,从仿真结果可以看出,在GPS短时失效的情况下,该方法有效抑制了姿态角、速度和位置的发散现象,提高了组合导航系统的精度和可靠性.     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

Minimum Error Entropy Based EKF for GPS Code Tracking Loop

This paper investigates the minimum error entropy based extended Kalman filter (MEEKF) for multipath parameter estimation of the Global Positioning System (GPS). The extended Kalman filter (EKF) is designed to give a preliminary estimation of the state. The scheme is designed by introducing an additional term, which is tuned according to the higher order moment of the estimation error. The minimum error entropy criterion is introduced for updating the entropy of the innovation at each time step. According to the stochastic information gradient method, an optimal filer gain matrix is obtained. The mean square error criterion is limited to the assumption of linearity and Gaussianity. However, non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian cases. Most of the existing multipath estimation algorithms are usually designed for Gaussian noise. The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler. One reasonable way to obtain an optimal estimation is based on the minimum error entropy criterion. The MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the residual. Performance assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.     

一种信标水声定位的改进扩展卡尔曼滤波算法

水声测距误差通常偏离高斯分布,纯距离扩展卡尔曼滤波(Extended Kalman Filter,EKF)定位跟踪算法误差较大。在将测距噪声分为高斯分量和非高斯缓变分量的基础上,提出了一种改进的扩展卡尔曼滤波EKF算法(Improved Extended Kalman Filter,IEKF)和初值选取方法。利用仿真实验和湖试对IEKF算法进行了验证,结果表明IEKF算法能够对测距偏差进行跟踪补偿,定位精度明显优于常规EKF算法。     

改进的EKF算法在目标跟踪中的运用

过程噪声和测量噪声影响Kalman滤波的性能,通常很难得到它们准确的值。提出观测噪声和过程噪声实时估计的自适应算法。该算法可以用在非线性和机动目标跟踪问题中,不必预先知道准确的噪声方差。重新估测观测噪声方差矩阵,可以较好地消除由观测噪声带来的误差;建立一个简单的线性Kalman滤波器对过程噪声进行实时估计,这对于机动目标来说是必要的,因为原有的过程噪声将受到加速度影响,不能包含全部的信息。实验表明,该算法保证EKF稳定性,提高了跟踪性能。模拟实验300次后,X,Y方向位置均方误差分别为7.8099,9.6838。     

基于二阶转换测量Kalman滤波的目标跟踪算法(英文)

为了减小传统跟踪滤波算法线性化误差,提高光电跟踪系统的跟踪速度和跟踪精度,本文在三维空间中,提出了二阶去偏转换测量卡尔曼滤波算法.该算法利用二阶泰勒展开的方法,推导出了光电跟踪系统观测方程的转换测量值误差的均值和协方差矩阵表达式,并对测量误差进行去偏差补偿处理,再经过转换测量卡尔曼滤波,可显著减小传统滤波算法的线性化误差.仿真结果表明,二阶去偏转换测量卡尔曼滤波(SCMKF)算法的跟踪精度优于非去偏转换测量卡尔曼滤波(CMKF)和扩展卡尔曼滤波(EKF),以及unscented卡尔曼滤波(UKF)算法,并且具 有更快的收敛速度,和采用统计方法的去偏转换测量卡尔曼滤波(DCMKF)的跟踪精度相当,但计算简单,提高了跟踪速度.     

EKF和PF算法及其在目标跟踪中的应用

基于贝叶斯滤波的目标跟踪原理,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)和粒子滤波(ParticleFilter,PF)的基本思想和算法实现步骤。在非线性环境下对比分析了EKF算法和PF算法的估计精度,并给出两种方法的适用条件。EKF算法采用Taylor展开的线性变换来近似非线性模型,而PF算法采用一些带有权值的随机样本来表示所需要的后验概率密度。仿真结果表明,在强非线性非高斯环境下,PF算法的跟踪性能远优于EKF算法,当系统非线性强度不大时,EKF算法和PF算法的估计精度相差不大,但PF算法计算复杂,跟踪时间长,实时性差。     

A novel intelligent fuzzy estimator for uniform load determination on a beam with varying boundary conditions

A novel intelligent fuzzy input estimation method that estimates the uniform input load on a beam structural system is presented in this article. The uniform input load acting on a beam structural system is estimated from the measured dynamic responses using the inverse method. The algorithm includes the fuzzy Kalman filter and the fuzzy-weighted recursive least square method. This study presents an efficient estimator accelerated and weighted using fuzzy-accelerating and -weighting factors proposed based on the fuzzy logic inference system. The capability of the proposed algorithm is demonstrated through several beam structural system examples with different types of boundary conditions. The simulation results are compared by alternating between the constant, adaptive, and fuzzy weighting factors. The results demonstrate that the presented method applied to beams with various structural system boundary conditions is successful.     

一种水下GPS系统及其在蛙人定位导航中的应用

研究了一种适用于蛙人导航的水下GPS系统。针对蛙人执行水下任务所需的高精度导航,提出了一种由主动声纳浮标作为定位基站的GPS定位系统,介绍了该系统基于延时测量的定位原理和求解方法,给出了Kalman滤波器和扩展Kalman滤波器的设计,并通过数值仿真进行了验证,结果表明：为提高定位精度,在定位解算的基础上进行滤波平滑是必要的。     

一种新的自适应非线性卡尔曼滤波算法

为避免由于系统噪声统计特性不准确所导致的滤波性能下降问题,改进了一种基于新息的系统噪声方差调整方法,并将其与扩展卡尔曼滤波、Unscented 卡尔曼滤波和差分滤波相结合,形成自适应非线性卡尔曼滤波.将此方法应用到非线性测量光电跟踪系统中,并与采用基本非线性卡尔曼滤波进行性能对比.仿真实验结果证明该方法可以实时调整系统噪声方差,有效地避免由于系统噪声统计特性不准确所带来的滤波性能下降的问题,而且其性能明显优于基本非线性卡尔曼滤波.     

The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments

Inertial-navigation system (INS) and global position system (GPS) technologies have been widely applied in many positioning and navigation applications. INS determines the position and the attitude of a moving vehicle in real time by processing the measurements of three-axis gyroscopes and three-axis accelerometers mounted along three mutually orthogonal directions. GPS, on the other hand, provides the position and the velocity through the processing of the code and the carrier signals of at least four satellites. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers several advantages and overcomes each of their drawbacks. The integration of INS and GPS is usually implemented utilizing the Kalman filter, which represents one of the best solutions for INS/GPS integration. However, the Kalman filter performs adequately only under certain predefined dynamic models. Alternatively, this paper suggests an INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements. The proposed method suggests two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA). Both architectures were developed utilizing multilayer feed-forward neural networks with a conjugate gradient training algorithm.     

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge(SOC) estimation of lithium-ion(Li-ion) battery are the key techniques of battery pack management system(BMS) and critical to its reliability and safety operation.An auto-regressive with exogenous input(ARX) model is derived from RC equivalent circuit model(ECM) due to the discrete-time characteristics of BMS.For the time-varying environmental factors and the actual battery operating conditions,a variable forgetting factor recursive least square(VFFRLS)algorithm is adopted as an adaptive parameter identification method.Based on the designed model,an SOC estimator using cubature Kalman filter(CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure.In the battery tests,experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter(EKF) algorithm,which is widely used for Li-ion battery SOC estimation,and the maximum estimation error is about 2.3%.     

多普勒计程仪的数据降噪算法

多普勒计程仪输出的船速数据中含有偏差较大的点,即野点,在数据处理时必须将其去除,否则可能会导致组合导航系统中的卡尔曼滤波发散。同时输出数据由于受到随机误差的影响,会导致数据的平滑性能变差。提出了一种多普勒计程仪的数据降噪算法,该算法首先利用改进的中值滤波方法去除数据中的野点,再利用小波阈收缩去噪算法去除随机误差。仿真结果表明,与传统的中值滤波相比,该算法能极大地提高处理增益,并且有很高的应用价值。     

一种改进的UKF算法及其在深空自主光学导航中的应用

利用星敏感器以及光学导航相机,通过测量星光信息以及天体边缘信息,进行了自主光学导航方案的设计.通过观测量的转化改进了Unscented卡尔曼滤波方法(UKF)的具体实现形式,并将改进的方法与扩展的卡尔曼滤波方法(EKF)、UKF以及基于平方根分解的方法(SR-UKF)进行了比较,通过仿真对其算法的优越性进行了验证.仿真结果表明,这种基于观测量转换的UKF算法,不仅在计算量上有所减少,在精度上也有较大提高.     

光电跟踪系统非线性新息自适应卡尔曼滤波算法

为解决非线性部分状态卡尔曼滤波算法中由于线性化误差所导致的滤波精度下降问题,提出采用UT变换方法计算系统状态误差方差,及基于新息自适应调整系统噪声方差,进而构成一种新的非线性自适应部分状态卡尔曼滤波算法,并总结出详细算法结构.同时,将此方法应用到非线性测量光电跟踪系统中,并与U卡尔曼滤波和非线性部分状态卡尔曼滤波进行性...