水下导航数据融合方法

设计了一种基于多模型自动识别与转换的潜艇水下自适应卡尔曼滤波算法,并以此为基础,引入联邦卡尔曼滤波思想,结合模式识别技术,实现模型的自动识别、转换与跟踪,克服常规卡尔曼滤波对于变模型系统没有自适应能力致使在很多情况下误差太大或无法应用的缺点。仿真结果证明此方法可以复杂的潜艇水下综合导航的数据融合技术开辟一条新的有效途径。

计算极谱法同时测定镉镍的研究

采用普通极谱仪与计算机联机技术,用卡尔曼滤波处理以KSCN为支持电解质的Cd^(2+)和Ni^(2+)混合溶液的示波极谱重叠波。实验表明,极谱可逆波具有较好的加和性,可采用计算极谱方法对多组分(尤其是难以分开的重叠波)进行同时测定。

Multiple Kalman filters model with shaping filter GPS real-time deformation analysis

In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.

Cascade Kalman filter for gravity anomaly distortion correction based on second order potential model

Using a gravity anomaly covariance function based on the second-order Ganssian Markov gravity anomaly potential model, the state equation of a gravity anomaly signal is obtained in marine gravimetry. Combined with the system state equation and the measurement equation, a new method of the cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, an inverse Kalman filter is used to restore the gravity anomaly signal and high frequency noises first. Then an adaptive Kalman filter, which uses the gravity anomaly state equation as the system equation, is set to estimate the actual gravity anomaly data. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of the single inverse Kalman filter method.

New approach of Kalman filter to nonlinear system

In order to achieve higher accuracy in nonlinear/non-Gaussian state estimation, this paper proposes a new unscented Kalman filter (UKF). It uses a deterministic sampling approach. We choose the unscented transformation (UT) scaling parameters α=0.85, β=2, l=0 to construct 2n + 1 sigma points. These sigma points completely capture the mean and covariance of the Gaussian random variables of the nonlinear system Yi=F(Xi). Simulation results show that the posterior mean and covariance of the sigma points can achieve the accuracy of the third-order Taylor series expansion after having propagated through the true nonlinear system Yi=F(Xi). Extended Kalman filter (EKF) only can achieve the first-order accuracy. The computational complexity of UKF is the same level as that of EKF. UKF can yield better performance and higher accuracy than EKF.

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

DESIGN AND REALIZATION OF SINS/TWO-ANTENNA GPS INTEGRATED NAVIGATION SYSTEM

The strapdown inertial navigation system （SINS）/two-antenna GPS integrated navigation system is discussed. Corresponding error and the measurement models are built, especially the double differenced GPS carrier phase model. The extended Kalman filtering is proposed. And the hardware composition and connection are designed to simulate the SINS/two-antenna GPS integrated navigation system. Results show that the performances of the system, the precision of the navigation and the positioning, the reliability and the practicability are im proved.

APPLICATION OF INTERVAL KALMAN FILTER TO AN INTEGRATED GPS/INS SYSTEM

An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.

Federated UKF algorithm for mobile location estimation with TDOA/Doppler measurements

In order to enhance the location estimation performance of mobile station（MS）tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter （UKF） is presented. The proposed algorithm uses the Singer mobile statement model as the reference system, and the simplified UKF as the subfilters. The subfilters receive the two groups of independently detected time difference of arrival （TDOA） measurement inputs and Doppler measurement inputs, and produce local estimation outputs to the main estimator. Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule, and a global optimal or suboptimal estimation result is achieved. Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation. In the simulations, the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone. The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS, and has favorable performance in both root mean square error（RMSE） and mean error cumulative distribution function（CDF）.

A novel reaching law approach of quasi-sliding-mode control for uncertain discrete-time systems

A novel discrete-time reaching law was proposed for uncertain discrete-time system,which contained process noise and measurement noise.The proposed method reserves all the advantages of discrete-time reaching law,which not only decreases the band width of sliding mode and strengthens the system robustness,but also improves the dynamic performance and stability capability of the system.Moreover,a discrete-time sliding mode control strategy based on Kalman filter method was designed,and Kalman filter was employed to eliminate the influence of system noise.Simulation results show that there is no chattering phenomenon in the output of controller and the state variables of controlled system,and the proposed algorithm is also feasible and has strong robustness to external disturbances.

VEHICULAR NODE POSITIONING BASED ON DOPPLER-SHIFTED FREQUENCY MEASUREMENT ON HIGHWAY

Vehicular node positioning needs to be quick and precise on highway for safety considera-tion.In this paper,we present a novel and practical vehicular node positioning method which can achieve a higher accuracy and more reliability than the existing global-positioning-system-based po-sitioning solutions by making use of Doppler-shifted frequency measurements taken by vehicular node itself.This positioning method uses infrastructure nodes which are placed on the roadside every several kilometers as radiation sources to estimate the relative distances of vehicle to the infrastructure node.Through coordinate conversion,we get the absolute coordinates of vehicular node based on known absolute coordinates of infrastructure node.We also analyze the optimal distance of neighbor infra-structure nodes in order to ensure a high accuracy.In addition,simulation results demonstrate that the accuracy of our method with Extended Kalman Filtering(EKF) is superior to the method without EKF.

Blind Channel Equalization Algorithm Based on Dual Unscented Kalman Filter for Chaotic Multi-Input Multi-Output Communication Systems

To reduce channel noise,fading,and inter-user interference effectively in the chaotic communication systems with multi-user,a blind channel equalization algorithm based on dual unscented Kalman filter algorithm is proposed.Assuming that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model,two separate state-space representations are used for the signals and coefficients.Then two unscented Kalman filters are used to estimate chaotic signals and channel coefficients simultaneously.The simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed.

Autonomous determination of orbit for probe around asteroids using unscented Kalman filter

The observed images of the asteroid and the asteroid reference images are used to obtain the probe-to-asteroid direction and the location of the limb features of the asteroid in the inertial coordinate. These informa-tion in combination with the shape model of the asteroid and attitude information of the probe are utilized to ob-tain the position of the probe. The position information is then input to the UKF which determines the real-timeorbit of the probe. Finally, the autonomous orbit determination algorithm is validated using digital simulation.The determination of orbit using UKF is compared with that using extended Kalman filter (EKF), and the resultshows that UKF is superior to EKF.

New sigma point filtering algorithms for nonlinear stochastic systems with correlated noises

New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated noises. Based on the minimum mean square error estimation theory, the nonlinear optimal predictive and correction recursive formulas under the hypothesis that the input noise is correlated with the measurement noise are derived and can be described in a unified framework. Then, UKF and DDF with correlated noises are proposed on the basis of approximation of the posterior mean and covariance in the unified framework by using unscented transformation and second order Stirling＇s interpolation. The proposed UKF and DDF with correlated noises break through the limitation that input noise and measurement noise must be assumed to be uneorrelated in standard UKF and DDF. Two simulation examples show the effectiveness and feasibility of new algorithms for dealing with nonlinear filtering issue with correlated noises.

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.

Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.

E^2KF Based Joint Multiple CFOs and Channel Estimate for MIMO-OFDM Systems over High Mobility Scenarios

An enhanced extended Kalman filtering （E2KF） algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets （CFOs） and time-variant channel estimate for MIMO-OFDM systems over high mobility scenarios. It is unveiled that, the auto-regressive （AR） model not only provides an effective method to capture the dynamics of the channel parameters, which enables the prediction capability in the EKF algorithm, but also suggests an method to incorporate multiple successive pilot symbols for the improved measurement update.

Galerkin-based extended Kalman filter with application to CO2 removal system

The carbon dioxide removal system is the most critical system for controlling CO2 mass concentration in long-term manned spacecraft.In order to ensure the controlling CO2 mass concentration in the cabin within the allowable range,the state of CO2 removal system needs to be estimated in real time.In this paper,the mathematical model is firstly established that describes the actual system conditions and then the Galerkin-based extended Kalman filter algorithm is proposed for the estimation of the state of CO2.This method transforms partial differential equation to ordinary differential equation by using Galerkin approaching method,and then carries out the state estimation by using extended Kalman filter.Simulation experiments were performed with the qualification of the actual manned space mission.The simulation results show that the proposed method can effectively estimate the system state while avoiding the problem of dimensional explosion,and has strong robustness regarding measurement noise.Thus,this method can establish a basis for system fault diagnosis and fault positioning.

Gaussian particle filter based pose and motion estimation

Determination of relative three-dimensional （3D） position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional （2D） intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter （GPF） based on the panicle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter （EKF） and the unscented Kalman filter （UKF） to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.