Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.

Application of adaptive Kalman filter in rocket impact point estimation

In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According to the particle trajectory model,the adaptive Kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was extrapolated.The results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive Kalman filter and the filter variance can converge in a short period of time.Based on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

Adaptive Robust Tracking Control of Pressure Trajectory Based on Kalman Filter

When adaptive robust control（ARC） strategy based on backstepping design is applied in pneumatic servo control, accurate pressure tracking in motion is especially necessary for both force and position trajectories tracking ofrodless pneumatic cylinders, and therefore an adaptive robust pressure controller is developed in this paper to improve the tracking accuracy of pressure trajectory in the chamber when the pneumatic cylinder is moving. In the proposed adaptive robust pressure controller, off-line fitting of the orifice area and on-line parameter estimation of the flow coefficient are utilized to have improved model compensation, and meanwhile robust feedback and Kalman filter are used to have strong robustness against uncertain nonlinearities, parameter fluctuations and noise. Research results demonstrate that the adaptive robust pressure controller could not only track various pressure trajectories accurately even when the pneumatic cylinder is moving, but also obtain very smooth control input, which indicates the effectiveness of adaptive model compensation. Especially when a step pressure trajectory is tracked under the condition of the movement of a rodless pneumatic cylinder, maximum tracking error of ARC is 4.46 kPa and average tracking error is 0.99 kPa, and steady-state error of ARC could achieve 0.84 kPa, which is very close to the measurement accuracy of pressure transducer.

基于自适应强跟踪Kalman滤波的GNSS跟踪环路设计

为提高GNSS接收机跟踪环路在复杂环境下的跟踪性能,提出一种基于自适应强跟踪Kalman滤波(ASTKF)的跟踪环路,在传统跟踪环路的基础上,以鉴相器输出为观测量进行自适应强跟踪Kalman滤波,滤波结果用于计算导航滤波器的观测量,同时将伪码频率和载波多普勒频率反馈到码NCO和载波NCO,在ASTKF中使用基于卡方分布的渐消因子计算方法,提升跟踪环路鲁棒性。半物理仿真实验表明,相比于基于Kalman滤波的跟踪环路和基于强跟踪Kalman滤波(STKF)的跟踪环路,所提出方法在水平方向上的位置误差和速度误差减小20%以上,有效提高了卫星导航接收机的定位性能。

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.

Scheme of adaptive polarization filtering based on Kalman model

A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.

Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint

Temperature （T） and salinity （S） profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter （EnKF）. An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data （satellite remote sensing sea level anomaly （SLA）, the OSCAR velocity product and shipboard ADCP）. The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error （RMSE） of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.

Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.

Continuous rotation north-finding algorithm based on constrained adaptive Kalman filter

As an orientation measurement system,north-finder has been playing a significant role in both military and civilian fields of orientation and control.In this paper,to deal with drawbacks in the conventional north-finding systems,a dynamic strategy based on continuous rotation modulation to measure the rotational angular velocity of the earth is proposed.By modeling the dynamic error,optimizing the process constraint and estimating dynamic noise,a method combining delay compensation and hardware adjustment,and a constrained adaptive Kalman filter(CAKF)algorithm are designed for the north-finding strategy.According to simulation and experiments,the proposed algorithm can achieve the high-precision north-finding with robust and anti-noise performance.

Multi-sensor Hybrid Fusion Algorithm Based on Adaptive Square-root Cubature Kalman Filter

In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint.To simplify the design process and reduce the demand of the sensors,this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control.The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter(ATEKF).To adapt to the widely varying parameters,a sliding mode update is designed to make the ATEKF adaptive,and together with the use of an initial setting update and a vertical tire force adjustment,the overall system becomes more robust.In particular,the model aims to eliminate the effects of the over-constraint and the uneven weight distribution.The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation,and its performance is better than that of the treble extend Kalman filter.

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.

GPS Navigation Using Adaptive Kalman Filter for Maneuvering Vehicle

Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard Kalman filters is listed.The performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are used.The simulation is based on the almanac data of the GPS satellites to compute its feasibility during the simulation time and position on shape 8 track with continuous vehicle maneuvering. The goal is to obtain computationally efficient filter with reasonable accuracy for vehicle in maneuvering situation. The filter proposed is an alternative to the filter proposed in Ref. [1] with low computational burden.

Kalman Filter for Adaptive Antennas

Adaptive control algorithm ls a key technlque for an adaptive array. It ls necessary to find a fastand efficient aIgorithm ror gaining rapid interrerence suppression. Convergence speed of conventional gradient-based algoritkms ls extremely slow and very sensitive to elgenvalue spread of autocorrelation matrix. Thispaper presents the Kalman filtering method to adaptlve array processing. It has good transient response andachieves faster convergence speed,so it is msot su1table for complicated adaptive systems. We analyzed itsconvergence performance which proved to be related to initial value for P(0). Computer simulation by applying four elements array to null steering was carrled out. It ls shown that fast convergence needs only 2Mnumbers of iteration. The Iess the value was seIected for P(o),the slower convergence was found. Further,ifP(o) were too, small, the method would not be abIe to converge. On the other hand, bigger values of P(0)can not achieve more lmprovement in convergence per formance. Slmulation aIso discovered steady responsedenoted with SNIR dependent on the power of reference signai. These results demonstrate the validity and effectiveness 0f the Kalman type adaptlve antenna.

Adaptive Extended Kalman Filtering for Bearings-Only Targets Tracking Problem

An adaptive extended Kalman filtering (AEKF) is proposed for nonlinear control systems. For bearingsonly targets tracking problem, we present an adaptive extended Kalman filter which suits a nonlinear observation model and a linear dynamical model. Simulation results have shown that the adaptive extended Kalman filter for the passivetracking problem performs better than the original extended Kalman filter (EKF).

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

Fuzzy Adaptive Strong Tracking Cubature Kalman Filter

To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).

模糊自适应Kalman滤波算法在SINS/DR组合导航的应用

针对里程仪测量误差导致组合导航精度降低的问题,提出基于系统工作状态和滤波器新息状态相结合的模糊自适应算法。根据新息的变化确定模糊规则,修正里程仪输出增益,使新息始终保持在零均值附近,利用修正后的新息修正观测噪声方差,降低导航定位的偏差。仿真实验结果证明,该算法能够很好地提高组合导航定位的精度。