Strong Tracking Extended Kalman Filter Research Articles

Improved strong tracking extended Kalman filter for identifying load disturbances and model uncertainties of serial–parallel mechanism

Serial–parallel mechanisms have been applied in numerous fields; however, their estimation accuracies are challenged by load disturbances and model uncertainties. To solve this problem, a detailed mathematical model was developed for a serial–parallel mechanism. The model consists of three submodels: a feeding subsystem, an execution subsystem, and a drive subsystem. A novel method based on an improved strong tracking extended Kalman filter (IST-EKF) is proposed to estimate the speeds and positions of the serial–parallel mechanism. In this method, an identifying factor is designed to identify errors caused by load disturbances and model uncertainties. Thereafter, a suboptimal fading scaling factor is proposed to manage errors of different forms. Furthermore, the suboptimal fading scaling factor can keep the symmetry of an error covariance matrix. A numerical simulation and an experiment with a serial–parallel mechanism of a 3PTT-2R NC machine tool were conducted to verify the effectiveness of the proposed IST-EKF.

Strong tracking extended particle filter for manoeuvring target tracking

To improve the stability and accuracy of manoeuvring target tracking in three-dimensional space based on the angle of arrival (AOA) and its rate of change observations, this study presents a new observation fusion method by fusing the received signal strength (RSS) with AOA and the rate of change of AOA. To enhance the adaptive ability of traditional strong tracking extended particle filter (TSTEPF) against model mismatch, this study re-determines the position of the fading factor in the strong tracking extended Kalman filter based on the orthogonal principle and gives the calculating method. And by combining the method with the particle filter, a new strong tracking extended particle filter (STEPF) algorithm is proposed. Simulation results show that after fusing RSS into the observation model, the tracking speed and precision are both improved, especially precision, as the position root-mean-square error has a 58% decline on average. And it is found that STEPF proposed in this study has a more stable adaptive ability than TSTEPF, and is superior in terms of position, velocity, and acceleration estimation accuracy.

Research on SOC balance strategy of STEKF battery based on MMC-BESS model

Under the condition of the battery current fluctuation and the uncertain initial value of the battery SOC in the MMC(Modular Multilevel Converter) battery energy storage system, there are relatively big errors in the prediction of SOC estimation values and there are poor results of the battery SOC balance, by using the ampere-hour integration method and the open-circuit voltage method. The method of model prediction was adopted to study the SOC balance strategy. Firstly, the power transfer function between each port is derived and the second-order Thevenin equivalent nonlinear model of the battery is established. On this foundation, the more accurate estimation results of the battery SOC are obtained employing the strong tracking extended Kalman filter algorithm, under the condition of the large fluctuation of the battery current and the uncertainty initial value of the battery SOC in the MMC-BESS model. In the end of the paper, the validity of the method introduced in the paper way was confirmed according to the simulation results of the simulation calculation based on the power transfer relationship between the battery SOC and each port, using the three-level SOC balance strategy to balance the battery SOC.

A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine

Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules. Because the health parameters are unmeasurable, researchers estimate them only based on the available measurement parameters. Kalman filter-based approaches are the most commonly used estimation approaches; however, the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty, and their ability to track the mutation condition is influenced by historical data. Therefore, in this paper, an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter (STEKF) approach is proposed to estimate the gas turbine health parameters. The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches. The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.

Model-based fault diagnosis of Lithium-ion battery using strong tracking Extended Kalman Filter

The failure of battery could lead to serious inconvenience, performance deterioration, accelerated aging and costly maintenance. It is imperative to design a reliable and robust fault detection scheme to detect the underlying faults and improper operation. In this paper, a model based fault diagnosis method is proposed based on multiple equivalent circuit models (ECMs) and strong tracking extended Kalman filter (ST-EKF), where the multiple ECMs are designed to capture different failure modes. The ST-EKF is utilized to estimate the terminal voltages and generate residual signals online. Simulation experiments are used to validate the effectiveness of the proposed algorithm in detecting multiple battery fault scenarios.

Symmetric-Strong-Tracking-Extended-Kalman-Filter-Based Sensorless Control of Induction Motor Drives for Modeling Error Reduction

This paper proposes a real-time speed identification method by using a symmetric strong tracking extended Kalman filter (SSTEKF) for induction motor sensorless drive. In SSTEKF, the residual sequences are forced orthogonal to each other, and the gain matrix is tuned in real-time by introducing fading factors into the covariance matrix of the predicted state. The modeling error is reduced, and the mutational state is tracked rapidly based on SSTEKF. Simultaneously, the Cholesky triangular decomposition is used to change the working way of the multiple fading factor matrix in the error covariance matrix. The application of the Cholesky triangular decomposition guarantees that the error covariance matrix is symmetric in the process of iteration, and the stability of the algorithm is enhanced. Therefore, the estimation accuracy, the tracking speed, and the noise suppression of the proposed method are better than the EKF. The correctness and effectiveness of the proposed method are verified by experimental results.

A hybrid positioning strategy for vehicles in a tunnel based on RFID and in-vehicle sensors.

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle positioning. How to achieve such positioning performance in extended GPS-denied environments such as tunnels is the main challenge for land vehicles. This paper proposes a hybrid multi-sensor fusion strategy for vehicle positioning in tunnels. First, the preliminary positioning algorithm is developed. The Radio Frequency Identification (RFID) technology is introduced to achieve preliminary positioning in the tunnel. The received signal strength (RSS) is used as an indicator to calculate the distances between the RFID tags and reader, and then a Least Mean Square (LMS) federated filter is designed to provide the preliminary position information for subsequent global fusion. Further, to improve the positioning performance in the tunnel, an interactive multiple model (IMM)-based global fusion algorithm is developed to fuse the data from preliminary positioning results and low-cost in-vehicle sensors, such as electronic compasses and wheel speed sensors. In the actual implementation of IMM, the strong tracking extended Kalman filter (STEKF) algorithm is designed to replace the conventional extended Kalman filter (EKF) to achieve model individual filtering. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy.

Application of STEKF in X-ray Pulsar Based Autonomous Navigation

The navigation of X-ray pulsar depend on the signal of X-ray pulsar radiation as an information input, through the corresponding signal and data processing, provide for navigation information of spacecraft position, velocity and attitude for near-Earth orbit, deep space and interstellar space flight, to achieve high-precision autonomous navigation of spacecraft. This paper introduces the principle of pulse satellite navigation established on the basis of the X-ray pulsars, autonomous navigation system, the state equation and observation equation. Select the three pulsars, using extended Kalman filter (EKF) and strong tracking extended Kalman filter (STEKF) algorithm, for satellite operation of the position and velocity estimation carried out simulation experiments. Simulation results show STEKF through the introduction of fading to dynamically adjust the gain matrix, to extract the maximum output of the residuals of all the useful information, making adaptive filter to correct the estimated bias and the ability to fast track the state changes, overcome the EKF defects in filtering performance degradation caused by the more complex non-linear navigation model due to uncertainty. Therefore, STEKF has higher valuation accuracy, numerical stability and strong traceability than EKF,and broad applicability in the autonomous celestial navigation.