Characteristics Of Process Noise Research Articles

An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries

Accurate state-of-charge (SOC) estimation is essential for fully utilizing the battery performance of electric vehicles. Considering the demand for algorithms with the advantages of simplicity, fewer calculations, good stability, and high accuracy in practical applications, this paper proposes a novel error covariance correction-adaptive extended Kalman filter (ECC-AEKF) for accurate and robust SOC estimation. In this paper, the maximum likelihood function of the probability density function of the error series (conditional on the priori covariance) is calculated by mathematical derivation to obtain a new priori error covariance, which is used to obtain a more appropriate Kalman gain. The ECC-AEKF can minimize the estimation error and reduces the effect of process noise characteristics and inappropriate error covariances on priori estimates. Meanwhile, a piecewise forgetting factor recursive least square (PFFRLS) is presented for model parameter identification. The PFFRLS using error feedback for real-time adaptively adjusts the forgetting degree of data based on the principle of integral separation. Furthermore, a comparative analysis of SOC estimation in PFFRLS-ECC-AEKF and commonly used methods is presented for validation of the performance of the proposed method under different temperatures and operating conditions. The results prove that the PFFRLS-ECC-AEKF achieves higher accuracy with less computation time than other methods.

Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

The optimal performance of the Kalman filters is highly dependent on the measurement and process noise characteristics, making the whole system unable to achieve the desired estimation in the presence of non-Gaussian mean noise distribution and high initial uncertainties. Recently, the H-infinity filter, as a robust algorithm, has been broadly used, as it is not being dependent on the pre-knowledge of the noise nature; however, making a balance between high robustness and estimation accuracy is a challenging issue. Hence, to overcome this problem, a new adaptive H-infinity extended Kalman filter (AHEKF) was designed in this paper, which benefits from both high robustness and precision. The suggested algorithm contains two adaptive sections to achieve high accuracy as well as controlling the effects of time-varying noise characteristics, high initial uncertainties, and abnormal data that can degrade the accuracy of state estimation in an integrated navigation system. The presented algorithm was used to integrate data from two independent sensors data. The simulation results for an inertial navigation system (INS)/global positioning system (GPS) sensor fusion are presented and compared with the standard H-infinity filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF) to show the effectiveness of the proposed algorithm. Evaluations demonstrate that the AHEKF achieves over 50% higher accuracy and robustness, and over 2.5 times faster convergence of estimation errors than the standard H-infinity filter.

Research on Autonomous Underwater Vehicle Homing Method Based on Fuzzy-Q-FastSLAM

Abstract Autonomous docking guidance is one of the key technologies to achieve the autonomous underwater vehicle (AUV) docking with the sub-sea docking station (DS) to realize long-term resident operation. In the process of AUV docking, the combination of long-distance acoustic guidance based on acoustic sensor and terminal visual guidance based on camera is often adopted. However, affected by the accuracy of the navigation sensor and acoustic positioning sensor carried by AUV, as well as the ocean current, AUV cannot accurately know its own position and the position of the DS, resulting in a large acoustic guidance error and the inability to enter the visual guidance stage with a reasonable deviation, thus leading to the docking failure. In this article, an improved FastSLAM algorithm is proposed to estimate the position of AUV and DS simultaneously. The positioning accuracy of traditional FastSLAM algorithm is affected by such factors as the estimation accuracy of the statistical characteristics of process noise. An improved algorithm for FastSLAM based on fuzzy Q-learning is proposed. The homing path is planned based on the Dubins theory. The path is tracked by line-of-sight guidance. The results of matlab simulation and experimental data analyzing of the portable AUV are applied to verify the effectiveness of the proposed algorithm.

Improved adaptive unscented Kalman filter algorithm for target tracking

An adaptive unscented Kalman filter (AUKF) algorithm is proposed to solve the problem that the statistical characteristics of the process noise are unknown in the target tracking, which leads to filter divergence or low filtering precision. The improved Sage-Husa estimator is used to estimate the statistical characteristics of the unknown process noise in the filtering process, and to judge and suppress the filtering divergence, which effectively improves the numerical stability of the filtering and reduces the error of the state estimation. The simulation results show that the improved AUKF algorithm not only keeps convergence but also improves the accuracy and stability of the target tracking under the condition of unknown time-varying process noise statistic, compared with the standard UKF algorithm.

Multistage manufacturing process control robust to inaccurate knowledge about process noise

A method for robust control of quality errors in a multistage manufacturing process (MMP) is formulated, guaranteeing the product quality even under the worst-case scenario of uncertainties in the knowledge of noise characteristics. Simulations based on the error flow models in automotive cylinder head machining and lithography overlay processes show that the new robust control method increasingly outperforms the traditional stochastic control approach as uncertainties in the knowledge of process noise characteristics increase. It is also shown that controlling MMP quality, while ignoring inaccuracies in the noise model could make quality even worse than if no control is used.

Spatial and temporal characteristics of optimum process noise values of tropospheric parameters for kinematic analysis of Global Navigation Satellite System (GNSS) sites in Japan

Kinematic analysis of Global Navigation Satellite System (GNSS) data is useful for the extraction of crustal deformation phenomena occurring over short timescales ranging from seconds to 1 day, such as coseismic and postseismic deformation following large earthquakes. However, a fundamental challenge in kinematic GNSS analysis is to separate unknown parameters, such as site coordinate and tropospheric parameters, due to the strong correlation between them. In this study, we assessed the spatial and temporal characteristics of process noise for unknown tropospheric parameters such as zenith wet tropospheric delay and tropospheric gradient by means of kinematic precise point positioning analysis using Kalman filtering across the Japanese nationwide continuous GNSS network. We estimated kinematic site coordinate time series under different process noise combinations of zenith wet tropospheric delay and tropospheric gradient. The spatial distribution of the optimum process noise value for the zenith wet tropospheric parameter with vertical site coordinate time series clearly showed regional characteristics. In comparison with the wet tropospheric parameter, the spatial characteristics of the tropospheric gradient parameter are less well defined within the scale of the GNSS network. The temporal characteristics of the optimum process noise parameters for each site coordinate component at specific sites indicated a clear annual pattern in the tropospheric gradient parameter for the horizontal components. Finally, we assessed the effects on the kinematic GNSS site coordinate time series of optimizing tropospheric parameter process noise. Compared with recommended process noise values from previous studies, the use of estimated “common” optimum process noise values improved the standard deviation of coordinate time series for the majority of stations. These results clearly indicate that the use of appropriate process noise values is important for kinematic GNSS analysis.

A Single Parameter Tunable Quaternion Based Attitude Estimation Filter

: In human posture tracking applications, limb segment attitude can be estimated without the aid of a generated source using small inexpensive inertial/magnetic sensor modules. In the absence of an adequate process model and process noise characteristics or in an application in which the dynamic and measurement relations are non-linear, a simple complementary filter represents a computationally inexpensive solution that produces accurate attitude estimates superior to those of an improperly tuned Kalman filter. This paper presents the theory and experimental results for a single parameter tunable quaternion based attitude estimation filter. The described sub-optimal constant gain complementary filter is the first filter designed for inertial/magnetic sensor modules capable of estimating orientation in all attitudes without singularities while continuously correcting for drift without the need for still periods. Experimental results qualitatively and quantitatively demonstrate the accuracy and stability of the filtering algorithm.