Kalman Filter Method Research Articles

Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network.

The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) has gained significant attention in the field of health management of power electronic equipment. The performance degradation of the IGBT gate oxide layer is one of the most important failure modes. In view of failure mechanism analysis and the easy implementation of monitoring circuits, this paper selects the gate leakage current of an IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter, and other methods to carry out feature selection and fusion. Finally, it obtains a health indicator, characterizing the degradation of IGBT gate oxide. The degradation prediction model of the IGBT gate oxide layer is constructed by the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) Network, which show the highest fitting accuracy compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and CNN-LSTM models in our experiment. The extraction of the health indicator and the construction and verification of the degradation prediction model are carried out on the dataset released by the NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. These results show the feasibility of the gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.

Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

The real-time inverse estimation of the ocean wave spectra and elevation from a vessel-motion sensor is highly valuable, although it is still in the early stages of development. The Kalman filter method using motion measurements has advantages over other methods in terms of real-time capability, cost savings, and easy installation. In this paper, the Kalman filter method is developed for the inverse estimation of multi-directional waves in real time. The problematic divergence in the high-frequency region encountered by previous papers using similar approaches is resolved by employing the Wiener filter method. The developed method is also shown to be robust against potential sensor noises. The real-time multidirectional-wave spectrum and elevations can be recovered, including the high-frequency region, using the developed methodology with the numerically generated motion-sensor signals with noise. The developed Kalman filter algorithm is tested and validated for various sea states of uni- and multi-directional waves. The inverse estimation of multi-directional waves is generally less accurate compared to that of unidirectional waves, mainly due to the significant increase of unknowns (component waves) in the Kalman filter processes. Nevertheless, the overall wave spectra, elevation time series, significant wave heights, peak period, main direction, and directional spreading were well recovered.

State of Charge Estimation for Lithium‐Ion Batteries Based on an Adaptive Fractional‐Order Cubature Kalman Filter

AbstractBased on the fractional‐order model (FOM), this paper proposes an adaptive fractional‐order cubature Kalman filter (AFCKF) method for state of charge (SOC) estimation of a lithium‐ion battery (LIB). Firstly, a FOM with two constant phase elements is built, which can accurately represent the dynamic features of a LIB with a higher accuracy. Secondly, the adaptive estimations of the coefficients in the measurement equation are achieved by a linear Kalman filter algorithm, which avoids the calculation of the relationship between the open‐circuit voltage and SOC. Thirdly, an augmented state equation including the SOC, the fractional‐orders and parameters in the FOM is investigated by introducing the augmented vector method, and the state information is estimated online via the AFCKF algorithm. The algorithm requires a little computational burden while ensuring the estimation accuracy and is well adapted to complex working conditions. Besides, this study fully considers the impact of noises on the estimation effect. To better overcome the disturbances caused by unknown noises and further improve the precision and stability of the algorithm, an adaptive estimation method of the noise covariance matrices is achieved. Finally, the experimental findings are given to reveal that the proposed method can be effectively used to different working conditions and the estimation accuracy is better than the adaptive integer‐order cubature Kalman filter.

Measurement performance evaluation and noise compensation for a Fourier transform infrared–based pyrometer

The Fourier transform infrared (FTIR) spectrometer is widely used for spectrum analysis and radiation intensity measurement. In this work, a pyrometer with the temperature measurement range of 800°C to 1200°C is developed based on the FTIR spectrometer, and the calibration of the instrument coefficients using a standard radiation source is performed. On this basis, the readout noise and shot noise in the spectral signal are analyzed, and a detailed quantitative evaluation of the influences of two kinds of noise on the temperature measurement accuracy is also carried out in this work. Experimental results show that the temperature deviation caused by the two kinds of noise is less than 1°C when the wavelength is within the range of 2.0 to 5.4 μm. However, when the wavelength is greater than 5.4 μm, the maximum temperature deviation caused by the readout noise and shot noise can reach about 12°C and 18°C, respectively. In view of this, a Kalman filter method is introduced in this work to reduce temperature fluctuations caused by these noises, where the maximum fluctuation can be reduced from 83.9°C to 6.1°C with this filter method. These experimental results can provide a valuable guidance for the general-purpose calibration and assessment of the FTIR-based pyrometer.

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

Implicit inverse force identification method for vibroacoustic finite element model

The two-field vibroacoustic finite-element (FE) model requires a relatively large number of degrees of freedom compared to the monophysics model, and the conventional force identification method for structural vibration can be adjusted for multiphysics problems. In this study, an effective inverse force identification method for an FE vibroacoustic interaction model of an interior fluid–structure system was proposed. The method consists of: (1) implicit inverse force identification based on the Newmark-β time integration algorithm for stability and efficiency, (2) second-order ordinary differential formulation by avoiding the state-space form causing large degrees of freedom, (3) projection-based multiphysics reduced-order modeling for further reduction of degrees of freedom, and (4) Tikhonov regularization to alleviate the measurement noise. The proposed method can accurately identify the unmeasured applied forces on the in situ application and concurrently reconstruct the response fields. The accuracy, stability, and computational efficiency of the proposed method were evaluated using numerical models and an experimental testbed. A comparative study with the augmented Kalman filter method was performed to evaluate its relative performance.

An automated driving systems data acquisition and analytics platform

In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects’ Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multi-CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high-definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects’ coordinates in Frenet coordinates and the lane information. In addition, to refine trajectories from the object tracking algorithms, a post-processing method is proposed. Given the objects’ information from the object detection and tracking and the world model, a Kalman filter and Chi-square test method are applied to reduce the noise and remove the outlier in the trajectories. Aiming at tackling the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories belonging to the same object. Then, a vehicle-kinematics-based trajectory prediction method is used, and a forward–backward-smoothing technique is applied to reconstruct the trajectory between the discontinuous trajectories. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the improvements by the post-processing algorithm in terms of noise level and outlier removal are discussed, which confirm the functionality and effectiveness of the proposed holistic data collection and processing platform. In another aspect, the extracted objects’ information and generated HD maps can be used for several purposes in the transportation research community and ADS development community: analyzing the interaction between human-driven vehicles and ADS-equipped vehicles, car-following behavior analysis of ADS-equipped vehicles, traffic flow status analysis and modeling, and scenario generation for ADS testing.

Pose-Dependent Cutting Force Identification for Robotic Milling

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

An Enhanced Fusion Algorithm With Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

An enhanced dual extended Kalman filter method is presented in this article for estimating and tracking the state-of-temperature of lithium-ion battery cells. A simple but effective dynamic and measurement empirical fit models are proposed and utilized to estimate the state-of-temperature concurrently with the state-of-charge. The proposed dual estimator improves the estimation accuracy of the temperature state by accounting for the variations in the state-of-charge. To test the performance of the proposed estimation method, two independent lithium-ion battery cell datasets were used to derive the empirical models and run the estimation algorithm. The obtained results show a promising performance of the estimation method in terms of the high estimation accuracy even in the case when the measurement contains high-magnitude noise or when the estimation algorithm is inaccurately initialized. The proposed models and the estimation algorithm are derived and experimentally tested in this article.

State of charge estimation of Li-ion batteries based on deep learning methods and particle-swarm-optimized Kalman filter

The estimation of SOC is a key issue for the high-efficient and reliable operation of Li-ion batteries, thus has been increasingly concerned in current years with the development of electric vehicles. During dynamic test cycles, the accurate estimation of SOC is more difficult than steady operation conditions due to the fierce oscillations of the input signals. In this paper, a hybrid method of deep learning method and Kalman filter was proposed for the estimation of SOC. First, convolutional neural network or temporal convolutional network was combined with different variants of recurrent neural network, including long short term memory, gated recurrent unit, peeple hole long short term memory and bidirectional long short term memory, to achieve the estimation of SOC by capturing the spatial and temporal characteristics of input signals. Afterwards, the deep learning method was integrated with Kalman filter to eliminate the effects of transient signal oscillations and further improve the accuracy for the estimation of SOC. The results indicated that estimation accuracy and estimation time could be improved by less than 20 % by varying deep learning methods while after integrating deep learning method with Kalman filter, more than 45 % improvements in test accuracy could be achieved without obvious sacrifices in estimation time.

Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point

Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the research area of RUL prediction. However, three dispiriting shortcomings in KF methods are still unavoidable, including: (1) difficulty in tracking the unknown time-varying noise information, (2) the subjectivity for setting time to start prediction (TSP), and (3) short-term accuracy of the predicting results based on linear predictors. To improve the capability of KF methods, this work adopts the variational Bayesian technique to adaptively describe noise information and considers linear and nonlinear factors of multi-channel signals to recognize the degradation stage transition point of bearing as TSP. Moreover, this work proposes an implicit Kalman filtering method to predict the RUL. The effectiveness of the proposed method is validated on XJTU-SY and IMS-Rexnord bearing data. Results show that the proposed method can recognize the TSP and improve the long-term accuracy of the prediction result during the accelerated degradation stage.

BI-IEnKF coupling model for effective source term estimation of natural gas leakage in urban utility tunnels

As an effective way to facilitate the increasing demand for reliable infrastructure, energy supply and sustainable urban development, underground utility tunnels have been developed rapidly in recent years. Due to the widespread distribution of utility tunnels, the safe operation of natural gas pipelines accommodated in utility tunnels has caused great concern considering fire, explosion, and other coupling consequences induced by the gas pipeline leakage. However, the limited information on leakage source terms in accidental leakage scenarios could preclude timely consequence assessment and effective emergency response. In this study, a BI-IEnKF coupling source term estimation (STE) model is developed, with the combination of gas dispersion model, Bayesian inference (BI) and iterative ensemble Kalman filter (IEnKF) method, to achieve the effective source term estimation (including leakage location and leakage rate) and gas concentration distribution prediction. The newly developed model is first evaluated by the twin experiment with good reliability and accuracy. Furthermore, three contributing factors affecting the performance of the developed BI-IEnKF coupling STE model were investigated to assist parameter selection for practical use. Additionally, the novel application of mobile sensors serving as an alternative for fixed sensors is explored, and an application framework is sequentially given to guide the deployment of the developed coupling model in utility tunnels. The results show that the developed model has great performance in accuracy, efficiency and robustness, as well as the potential to be applied in actual utility tunnel scenarios. This study can provide technical supports for safety control and emergency response in the case of natural gas pipeline leakage accidents in utility tunnels. Also, it could be helpful to reasonable references for gas lekage monitoring system design.

Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier–Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods.

State-Estimation-Based Control Strategy Design for Connected Cruise Control With Delays

The research on connected and automated vehicles attracts much attention recently. In connected vehicle systems, connected cruise control (CCC) is a wildly concerned technique to improve traffic safety and mitigate congestion. In this article, an optimal state-estimation-based (SE-based) control algorithm is proposed in a backward recursion manner to regulate CCC vehicle's longitudinal motion under stochastic communication delays, state noises, and channel interferences. Based on the analysis of error dynamics of vehicles and optimal state estimation strategy, the SE-based system dynamics is first formulated by using the Kalman filtering method. Then, a two-step SE-based control algorithm aiming at minimizing the quadratic system cost is proposed to enhance the vehicular platoon stability. In particular, the optimal control strategy can be generated online as a linear function of the estimated state information and previous control signals, and the corresponding coefficient can be iteratively calculated in an offline step. Finally, the results are successfully extended to the case of arbitrary communication delays, and the condition for the asymptotically mean square stability of the proposed optimal SE-based CCC algorithm is derived. Simulation results indicate that the proposed SE-based control algorithm has better system performance and control stability compared with existing works.

Kinematics Analysis of Arms in Synchronized Canoeing With Wearable Inertial Measurement Unit

Synchronized canoeing is a cyclically repetitive sport, and two canoeists need to work in concert with each other’s power patterns and paddle movements to achieve great results. Most of the analyses of synchronized canoeing focus on the mechanics and work output of oars. This article proposes a motion-capture method based on inertial sensors. The extended Kalman filter method is used to fuse the sensor information and reconstruct human movements. Four sets of joint angular movement data of the shoulders and elbows of two canoeists are extracted, respectively, for kinematics analysis with correlation coefficient and dynamic time warping (DTW) method. The experimental results show that there is a significant correlation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r=0.9605$ </tex-math></inline-formula> ) between the shoulder joint angles of the synchronized canoeists. The DTW method can also clearly analyze the posture coordination of two canoeists. According to the analysis results of upper limb kinematics, this research can evaluate the synchronization effect of synchronized canoeing from the perspective of kinematics, help canoeists improve their technical movements, and help coaches reasonably select athletes with matching skills and styles.

Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter Method for State-of-Charge Estimation of Li-Ion Battery Cells

State-of-charge estimation is an essential part of a battery management system. Charging and discharging batteries involve complex chemical processes that could lead to undesired consequences, such as premature end of life or fire hazards if the battery's state-of-charge is not closely monitored. This work proposes a state-of-charge estimation method that compensates for noisy measurements and parametric uncertainties in a battery cell to improve the estimation accuracy. In this work, a robust and adaptive estimation scheme based on a Cubature Kalman filter is proposed. The algorithm utilizes the Variational Bayesian method to identify the measurements noise covariance magnitude online. Additionally, the algorithm is augmented with the ability to suppress outliers based on the Maximum Correntropy Criterion. The proposed filter is experimentally verified on standard tests including aggressive electric vehicle drive cycles, and it is compared against the extended Kalman filter, the Cubature Kalman filter, and the Variational Bayesian Cubature Kalman filter.

Rapid Detection and Orbital Parameters’ Determination for Fast-Approaching Non-Cooperative Target to the Space Station Based on Fly-around Nano-Satellite

Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation.

Reduced oriens-lacunosum/moleculare cell model identifies biophysical current balances for in vivo theta frequency spiking resonance.

Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful "tools" that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in in vivo-like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our in vivo-like scenarios. Applying spike-triggered average analyses, we were able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.

The ability to detect the linear attack of WL-CUSUM and FMA algorithms

The problem of detecting linear attacks on industrial systems is presented in this paper. The object is attacked by linear attack is the wireless communication process from sensors to controller with simulated mathematical model (stochastic dynamical systems and random noises). The attack matrices are calculated to ensure that Kullback-Leiber (K-L) algorithm is passed. With these matrices, the window limited cumulative SUM (WL-CUSUM) algorithm and finite moving average (FMA) algorithm are utilized to detect the changes in the sequence of residuals generated from Kalman filter method and are appreciated the ability to detect the linear attack. The simulated results show that an appropriate range of threshold of the WL-CUSUM and FMA algorithm can be chosen to detect the linear attack in case the K-L method cannot detect. Moreover, tested results using the Monte Carlo simulation also show that the evaluation performance of the FMA detection algorithm is better than that of WL-CUSUM, CUSUM, and Chi-squared (Chi2).

Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

&lt;span lang="EN-US"&gt;Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;i&lt;sup&gt;th&lt;/sup&gt;&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly.&lt;/span&gt;