Recent Developments in Kalman Filtering with Applications in Navigation

Inertial Navigation System (INS) and Global Positioning System (GPS) technologies have been widely used in a variety of positioning and navigation applications. Both systems have their unique features and shortcomings. Therefore, the integration of GPS with INS is now critical to overcome each of their drawbacks and to maximize each of their benefits. We present low-cost MEMS inertial and GPS sensor data fusion using a Kalman filter for vehicle distance and velocity estimation. Important parts of the developed Kalman filter that is used to optimally combine data from navigation sensors are described. Then, algorithm performance is illustrated based on an experiment during which the GPS/MEMS-IMU system was installed inside a car. Ill. 16, bibl. 3 (in English; summaries in English, Russian and Lithuanian).

Real-time reconstruction of the time-dependent heat flux and temperature distribution in participating media by using the Kalman filtering technique

Due to the advantages of small size and low cost, micro-electro-mechanical system (MEMS) inertial navigation systems (INS) show good prospects for use as a part of measurement-while-drilling (MWD) equipment to guarantee proper directional drilling procedure. Since current MEMS sensors have inaccurate measurements, an update aiding solution is developed using the Kalman filtering (KF) technique. However, because of the inherent poor behavior of MEMS sensors, KF technique with its linearized models has limited capability in providing accurate solution through the entire surveying process. In addition, certain realistic problems from the rugged environment would interrupt the updates in KF, without which the performance of the inertial system would deteriorate badly. This research proposes a fast orthogonal search (FOS)/KF solution where the FOS (a nonlinear modeling technique) method is proposed to augment KF. The experimental results illustrate that the FOS/KF solution outperforms the KF-only solution. Velocity and position performance are greatly enhanced during the interruptions of measurement updates.

In this study, the integrated navigation system, consisting of radio and inertial navigation system (INS) altimeters, is presented. INS and the radio altimeter have different benefits and drawbacks. The integration is achieved by using an indirect Kalman filter. Hereby, the error models of the navigators are used by the Kalman filter to estimate vertical channel parameters of the navigation system. In the open loop system, INS is the main source of information, and radio altimeter provides discrete aiding data to support the estimations. At the next step of the study, in case of abrupt faults, the performance of the integrated system is examined. The optimal Kalman filter reacts with abnormal estimates to this situation as expected. To recover such a possible malfunctioning, the robust Kalman filter algorithm is suggested.

Research on improved dynamic load identification method based on Kalman filter under noise interference

A local geopotential model for implementation of underwater passive navigation

An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

In a mobile Radio Frequency Identification (RFID) system, the RFID tag is attached to a mobile object such as a vehicle, a human etc. The information is more difficult to detect than in the case where the tag is attached to a stationary object. The RFID reader and back-end database both help to automatically record and store the vehicle driver information and real time status of a tracked geographic information system (GIS), and they cooperate to synchronize the information flow and monitor. In this paper, the Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. It also includes estimating the state of dynamic systems, almost all systems have some dynamic component, and determine how to best use a given set of vehicle sensors for modeling a high performance analysis by using the KF algorithm.

In the control system application, the existence of noise measurement may impact on the performance degradation. The noise measurement of the sensor is produced due to several reasons, such as the low specification, external signal disturbances, and the complexity of measured state. Therefore, it should be avoided to achieve the good control performance. One of the solutions is by designing a signal filter. In this paper, the design of Kalman Filter (KF) algorithm for ultrasonic range sensor is presented. KF algorithm is designed to overcome the existence of noise measurement on the sensor. The type of ultrasonic range sensor used is HC-SR04 which is capable to detect the distance from 2 cm to 400 cm. The discrete KF algorithm is implemented using ATMega 328p microcontroller on Arduino Uno board. The algorithm is then tested with different three covariance values of process noise. The test result shows that the KF algorithm is able to reduce the measurement noise of the ultrasonic sensor. The analysis of variance conducted shows that the smaller value of covariance matrix of the process and measured noises, the better filtering process performed. However, this results in a longer generated response time. Thus, an optimization is required to obtain the best filtering performance.

Characterization of Cubature Kalman Filter for GPS Delayed Environments in INS-GPS Integrated Navigation

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

Groundwater pollution source identification using Metropolis-Hasting algorithm combined with Kalman filter algorithm

Inspired by the advantages of hybrid intelligent optimization methods, this paper at first proposes a hybrid differential evolution with particle swarm optimization (DEPS) to solve a two-stage hybrid flow shops scheduling problem. On the basis of analyzing the convergence and optimization scheme of DEPS, the Kalman filter algorithm and a multi-stage learning strategy are then creatively fused into DEPS, namely KLDEPS, to enhance the running performance of the algorithm. The introduction of the Kalman filter enriches the diversity of individuals and enhances the neighborhood search ability of the algorithm, and the combination with the multi-stage learning strategy has beneficial effect on jumping out of the local optimal scheme. To make the proposed KLDEPS more suitable for a real manufacturing environment, the constraints of queueing time between two stages, different job sizes and processing time are imposed on the scheduling problem. The performance of the proposed KLDEPS is evaluated by comparing with two other high-performing intelligent optimization algorithms. The computational results reveal that the proposed KLDEPS outperforms the other two algorithms both in solutions’ quality and convergence rate.

The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for visual tracking problem. The VBI-based KF algorithm works to estimate the unknown measurement noise covariance matrix (MNCM) and process noise covariance matrix (PNCM), and the MCKF works to deal with non-Gaussian process noise and measurement noise. In order to cater for the visual tracking target scenario, where the MNCM and PNCM are unknown and non-Gaussian, we modify the VBI-based KF algorithm to work for suddenly occurring outliers and modify the MCKF work for small bandwidth. Simulations show that the proposed VBI-MCKF algorithm performs well when both covariance matrices of Gaussian process noise and non-Gaussian measurement noise are unknown or inaccurate.