Modified Extended Kalman Filter Research Articles

Hybrid Filter Scheme for Optimizing Indoor Mobile Cooperative Tracking System

The precise indoor tracking system using Xbee signal strength protocol has become a potential research to the WSN applications. The main aspects for the success tracking system is accuracy performance based on location estimation. The improvement of location estimation is complicated issue, especially using RSSI with low accuracy due to the signal attenuation from multipath effect at indoor propagation. Hence, many existing research typically focused on specific methods for providing improvement schemes at tracking system area. Then, we propose hybrid filter schemes, including extended gradient filter (EGF) for filtering noise signal based distance modification, and modified extended Kalman filter (MIEKF) will be combined with trilateration for filtering the error position estimation. Using mobile cooperative tracking scenario refers to our previous work, the proposed hybrid filter scheme which is called modified iterated extended gradient Kalman filter (MIEGKF) can optimize the error estimation around 41.28% reduction with 0.63 meters MSE (mean square error) value.

Hybrid position/force control of Stewart Manipulator using Extended Adaptive Fuzzy Sliding Mode Controller (E-AFSMC)

A new and effective control method is proposed for the hybrid position/force control of a Stewart Manipulator (SM). The control approach can be divided into the two parts, the first is an estimation of contact parameters between the robot and environment and the second one is the force control of manipulator according to estimated parameters. Hunt-Crossley nonlinear model is considered to describe the normal force in the contact region and Modified Extended Kalman Filter (MEKF) is used for estimating contact parameters. Extended Adaptive Fuzzy Sliding Mode Controller (E-AFSMC) is designed to hybrid position/force control of SM in the presence of state-dependent uncertainties. The proposed method is verified numerically, showing the effectiveness of the proposed method in critical situations such as actuator saturation, unexpected large disturbances, and state-dependent uncertainties.

A M-EKF fault detection strategy of insulation system for marine current turbine

Abstract Marine current power is a new renewable energy source, it is significant to keep the marine current turbine healthy. But harsh marine conditions, such as regular thermal cycling, high humidity and heavy salt mist, bring severe challenges to the marine current turbine, especially to the winding insulation system. Therefore, it is of great importance to perform fault detection on the insulation system. In this paper, a Modified Extended Kalman Filter (M-EKF) fault detection strategy based on the electrical parametric model derived from the RLC network modeling of Roebel bars is proposed. The proposed fault detection strategy mainly includes two parts. The first step is to establish reasonable state space equations for the model structure extracted to monitor the state of the insulation system. In the second step, the conventional continuous EKF is modified to follow more accurately the state of the marine current turbine. Simulation and experiment results show that the proposed method can realize timely monitoring of the winding insulation system of marine current turbine with good performance.

Kalman Filtering for Carrier Phase Recovery in Optical Offset-QAM Nyquist WDM Systems

Nyquist wavelength-division-multiplexing (NWDM) systems based on offset quadrature amplitude modulation (OQAM) have been considered as interesting candidates to achieve a better spectral efficiency. However, such systems are very sensitive to laser phase noise that causes the loss of OQAM orthogonality. In this letter, we investigate carrier phase recovery using a modified extended Kalman filter (M-EKF) in OQAM-based NWDM systems. The proposed method can achieve an optical signal-to-noise ratio (OSNR) penalty lower than that of the state-of-the-art modified blind phase search (M-BPS) algorithm. More specifically, for a $10^{-5}$ normalized laser linewidth, the M-EKF method exhibits 0.8- and 1.7-dB OSNR penalties (at a bit error rate of $3.8\times 10^{-3}$ ) lower than the M-BPS method for 16- and 64-OQAM modulations, respectively. Furthermore, compared with the M-BPS method, our proposed method is of lower complexity. The required total number of multiplications and additions of the M-EKF method is reduced by about 3.3 and 6.7 times compared with that of M-BPS for 16- and 64-OQAM modulations, respectively, confirming the effectiveness of the proposed method.

Stochastic stability of modified extended Kalman filter over fading channels with transmission failure and signal fluctuation

The design-oriented analysis based on the MEKF is presented over fading channels.The offline sufficient condition is provided for the MEKFs PECMS.The offline sufficient condition is derived for mean-square bounded estimate error.This work facilitates the design of the estimator over fading wireless networks. The observations of nonlinear systems, exposed to a fading channel, greatly suffer from both transmission failure and signal fluctuation. This paper focuses on the design-oriented analysis of nonlinear estimator based on a modified extended Kalman filter (MEKF) over fading wireless networks. Bernoulli process and Rayleigh fading are taken into consideration to model transmission failure and signal fluctuation, respectively. The offline sufficient conditions are established for the boundedness of the expectations of the prediction error covariance matrices sequence (PECMS) of the MEKF, which shows the existence of a crucial arrival rate. Furthermore, based on the derived upper bound of PECMS, further sufficient conditions are provided for mean-square bounded estimate error of the MEKF using the fixed-point theorem. Numerical examples are also given to verify the analytical results and demonstrate the feasibility of the proposed methods.

Estimation of Lévy Processes via Stochastic Programming and Kalman Filtering

The estimation of Levy process has received a lot of attention in recent years. Evidence of this is the extensive amount of literature concerning this problem which can be classified in two categories: the nonparametric approach, and the parametric approach. In this paper, we shall concentrate on the latter, and in particular the parameters will be estimated within a stochastic programming framework. To be more specific, the first derivative of the characteristic function and its empirical version shall be used in objective function. Furthermore, the parameter estimates are recursively estimated by making use of a modified extended Kalman filter (MEKF). Some properties of the parameter estimates are studied. Finally, a number of simulations will be carried out and the results are presented and discussed.

Modified unscented Kalman Filter for nonlinear systems having linear subsystems

The Extended Kalman Filter (EKF) is the often used filtering algorithm for nonlinear systems. But it does not usually produce desirable results. Recently a new nonlinear filtering algorithm named as Unscented Kalman Filter (UKF) is introduced. In this paper, we propose a new modified Unscented Kalman Filter (MUKF) algorithm for nonlinear stochastic systems that are linear in some components. These nonlinear systems can be considered as having linear subsystems with parameters and aim is to estimate the system parameters. In simulation study, performance of the EKF, its known variant Modified Extended Kalman Filter (MEKF), UKF and the proposed MUKF is demonstrated for a nonlinear system that is linear in some components. The results show that MUKF gives the best solution for parameter identification problem

A recursive algorithm for nonlinear least-squares problems

The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). The error of the MEKF algorithm is proved to be exponentially bounded. Batch and iterated versions of the algorithm are given, too. As an application, the algorithm is used to optimize the parameters in certain nonlinear input---output mappings. Simulation results on interpolation of real data and prediction of chaotic time series are shown.

Laboratory investigation of a multiple-model state estimation scheme for detection and isolation of leaks in pipelines

A pipeline system is an essential component in the transport of natural gases, crude and oil products in the petroleum industry, as well as gas and water in vital utility systems. Early detection of leaks in pipelines is essential to avoid excessive economical loss and reducing the environmental and health hazards that normally result from undetected leaks. In this paper, a model-based estimation scheme, which was developed as a basis for real-time monitoring of fluid flow in pipelines, is tested experimentally. In this estimation scheme, the fluid flow in a pipeline, which is modelled by a set of non-linear partial differential equations, is represented in a state-space form. A modified extended Kalman filter (MEKF), with its internal model defined by the obtained state-space form, is invoked together with feedforward computations to establish an adaptive multimodel state estimation technique. A laboratory experimental test-rig is constructed to test the validity and effectiveness of the developed leak detection and localization scheme. Experimental results utilizing the laboratory set-up are presented to demonstrate that the developed scheme effectively detects and locates leaks in pipelines within a short time duration.

THE ATTITUDE DETERMINATION ALGORITHM USING INTEGRATED GPS/INS DATA Yi-Min Huang

Traditionally, the extended Kalman filter (EKF) is a well known approach for general nonlinear data fusion problem. But the unit norm constraint of quaternion vector, which is the most efficient expression of attitude, results in the singularity of error covariance matrix. To overcome this difficulty, the modified extended Kalman filter (MEKF) has been developed by Shuster (1982). In order to improve the computational complexity of MEKF, we introduce the concept of spherical linear interpolation (SLERP). SLERP is a simple approach when two quaternions are averaged according to their accuracy dependent weightings. The SLERP based MEKF is proposed in this paper. However, its shortcoming is that the SLERP based MEKF cannot estimate gyro bias. Hence the proposed approach is not appropriate for the long duration navigation. The trade-off of MEKF and the SLERP based MEKF is discussed also.

A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation

Model-based monitoring schemes are known to have a great potential in detection and localization of leaks in pipelines. Fluid flow in pipelines is characterized by a system of non-linear-coupled partial differential equations. Since state estimation can provide the basis for real-time monitoring of fluid flow in pipelines, a suitable numerical scheme is employed to formulate the problem in state-space form, which enables the development of state estimation techniques. A modified extended Kalman filter (MEKF) in conjunction with feed forward computations to anticipate the leak magnitude provides the core of the adaptive multimodel state estimation technique used in this paper. Numerical simulation results show that the developed state estimation scheme effectively detects and locates small leaks in pipelines within a short time duration.

A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation

Model-based monitoring schemes are known to have a great potential in detection and localization of leaks in pipelines. Fluid flow in pipelines is characterized by a system of non-linear-coupled partial differential equations. Since state estimation can provide the basis for real-time monitoring of fluid flow in pipelines, a suitable numerical scheme is employed to formulate the problem in state-space form, which enables the development of state estimation techniques. A modified extended Kalman filter (MEKF) in conjunction with feed forward computations to anticipate the leak magnitude provides the core of the adaptive multimodel state estimation technique used in this paper. Numerical simulation results show that the developed state estimation scheme effectively detects and locates small leaks in pipelines within a short time duration.

Modified extended Kalman filtering

In this paper we compare the performance of the extended Kalman filter (EKF) with that of a proposed filter called modified extended Kalman filter (MEKF), in terms of the actual state and the estimated state as a function of time and the integral-squared error as a function of noise intensity both for dynamics and measurement noise. All the simulation results indicate that the performance of the MEKF is better than EKF. For illustration, numerical examples are presented using both EKF and MEKF. >

Parallel computation of the modified extended kalman filter

In this paper, we describe certain techniques for mapping the modified extended Kalman filter (MEKF) onto systolic array processors. First, we introduce a square-root algorithm based on the singular value decomposition (SVD) for the Kalman filter. Then, we develop a VLSI architecture of the systolic array type for its implementation. Compared with other existing square-root Kalman filtering algorithms, our new design is numerically more stable and has nicer parallel and pipelining characteristics when it is applied to the MEKF. Moreover, it achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(3/2n 2) processing elements and completes an iteration in time O((s + 8)n), in contrast to the time complexity of O((s + 3)n 3) for a sequential implementation, where s ≈ log n.