Joint Kalman Filter Research Articles

Applied Research on Intelligent Collection System of Positioning Information for Multi-source Heterogeneous Distribution Communication

Abstract In order to better realize the intelligent perception of fault location information of power distribution communication access networks in different scenarios, this paper carries out the construction of a power distribution communication network system through industrial Ethernet technology combined with EPON technology under fiber optic communication technology. Based on GIS technology and combined with OTDR technology, we constructed a fault location system for the fiber optic cable of the power distribution communication network so as to realize the acquisition of fault location information. From the multi-source state information, the joint Kalman filtering model is used to excavate the characteristics of the grid operation state, and then the fusion of multi-source heterogeneous data of power distribution communication is realized. To verify the effectiveness of the distribution communication network system constructed in this paper for locating fault information and intelligently collecting power consumption, system tests were conducted. The results show that the relative error of the node voltage estimation of the joint Kalman filter algorithm is below 0.015%, which improves the accuracy of fault identification in the distribution network. The system constructed in this paper for information transmission connectivity testing PON port light-emitting power range of 2~6dm, receiving optical power greater than -25dm, each PON port without power overload. This shows that the distribution automation communication network constructed in this paper meets the information collection requirements, and the construction of an intelligent distribution communication network can meet the requirements of distribution automation and can also accurately collect the fault location information of the distribution network.

A battery capacity estimation method based on the equivalent circuit model and quantile regression using vehicle real-world operation data

Battery capacity estimation is critical for evaluating the battery performance and safety. Currently, the sampling interval of battery operation data on the cloud is 10–30 s, and relatively poor data quality poses difficulties in battery capacity estimation. In this study, a battery capacity estimation method is proposed based on the battery equivalent circuit model and a quantile regression method using real-world vehicle operation data on the cloud. The battery parameters were identified based on the Thevenin battery model, and the state of charge (SOC) was estimated using a joint Kalman filter. Then, the discharge electric quantity–SOC data was fitted using quantile regression to obtain the preliminary capacity estimation results. To describe the battery capacity degradation along with driving mileage, this estimation result was further fitted using the Arrhenius empirical aging model and another quantile regression. The Nelder–Mead method was used to estimate the coefficients in quantile regression-related algorithms. Because quantile regression is not sensitive to the outliers of data points, it can reduce the impact of poor data quality on battery capacity estimation results. The proposed battery capacity estimation method was verified using real-world vehicle operation data, and the capacity estimation errors were within 3.2%.

A Cross-Layer Defense Method for Blockchain Empowered CBTC Systems Against Data Tampering Attacks

Due to the high integration of wireless communication and networking technologies, the communication-based train control (CBTC) systems are exposed to additional cyber-attack surfaces, allowing sophisticated attackers to combine cyber attack vectors with physical attack means to achieve malicious goals. Notably, the decentralized authentication features are missing in existing communication protocols which make the CBTC be easily compromised by data tampering attacks, and lead to serious operational accidents. With outstanding advantages in decentralized authentication, blockchain provides new effective solutions for decentralized identity authentication in CBTC. Consequently, it is critical to study the complex physical consequences of cyber breaches from a cross-layer defense perspective. In this paper, we propose a novel cross-layer defense method for cyber security in blockchain empowered CBTC against data tampering attacks. In the physical layer, the joint Kalman filter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2} $ </tex-math></inline-formula> detector is proposed for the train state estimation and detection. In the cyber layer, an asymmetric encryption-based secure communication protocol with identity authentication and the blockchain-based distributed key management system with the adaptive consensus mechanism are designed for data communication security. Considering the unavailable direct observation of the CBTC cyber security states, a partially observable Markov (POMDP) decision model is constructed to derive the optimal adaptive consensus strategies for balancing cyber security and efficiency. Extensive simulation results show that the proposed blockchain empowered CBTC cross-layer defense method can effectively improve the cyber security protection capability and minimize the impact of data tampering attacks on the train operation.

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering.

The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Accurate modeling of thermal characteristics is critical to the safe use and reliable management of lithium-ion batteries. However, limitations in sensors and testing methods make online real-time acquisition of internal temperatures extremely difficult. This paper uses the similarity of dynamic system modeling to construct a lumped thermal characteristic model of the battery. By analyzing the heat conduction mechanism inside the battery, the optimized heat path model is combined with the classical Bernardi equation to realize the state description of the battery thermal characteristic system. In addition, the forgetting factor recursive least squares algorithm is used to realize the online identification of the parameters of the lumped thermal characteristics model. Meanwhile, the identification of the external thermal resistance is coupled with the estimation of the internal temperature, and a novel online adaptive co-estimation strategy based on the forgetting factor recursive least squares — joint Kalman filter is proposed, which solves the problem that the external thermal resistance cannot be accurately identified adaptively in a complex environment. The experimental results show that the maximum root-mean-square error of the model under different experiments is 0.53 °C, which verifies the high-accuracy of the lumped thermal characteristics modeling strategy.

An Adaptive Unscented Kalman Filter With Selective Scaling (AUKF-SS) for Overhead Cranes

This article introduces an augmented adaptive unscented Kalman filter (KF). The proposed novel technique is suitable to simultaneously estimate both the diagonal process noise covariance matrix and the unknown inputs, thus combining previously reported KF estimators for unknown inputs (dual or joint KF) and for covariance matrices (adaptive KF). A selective scaling method is also introduced to improve the convergence property of the suggested KF. The development of the novel KF is also motivated by a specific estimation problem related to crane systems. Cranes represent a special class of weight handling equipment as they are underactuated and described by nonlinear dynamics such that the load present oscillatory behavior. In addition to the increasing need for their automation in various industrial fields, these features also make them a benchmark system in control engineering with numerous control laws reported in the literature for sway elimination and trajectory tracking. A common issue to realize most of the advanced control laws on real, eventually industrial size cranes is the necessity to know the sway angle and frictions on the configuration variables. It is shown in simulation and also with real experiments on a reduced size overhead crane system that the suggested KF is suitable to estimate both the sway angles and the frictions.

The Methods of Information Acquisition and Information Fusion of the Photoelectric Sensor-Based Quadrotor Unmanned Aerial Vehicle

When quadrotor unmanned aerial vehicle (UAV) is performing various tasks, even a small angular error will affect the evaluation of the entire motion trajectory. The multiple photoelectric sensor information fusion technology and the ARM microprocessor platform are used to form an attitude reference system for UAV. First, the hardware design of the small quadrotor UAV attitude reference system based on an ARM is introduced. The design framework and information acquisition module are expounded. In terms of the software of the system, the photoelectric sensor is used to receive different kinds of information, and the dynamic loading component is adopted as the solution to the interface diversification problems. Based on the attitude reference system, the collected information needs to be fused. The Kalman filtering is taken as the research object. Combined with the multiple photoelectric sensor information fusion technology, the Kalman filtering method is improved in the data preprocessing, and the low-pass filtering is added. Therefore, the abnormal data is filtered, and the estimated values are converged in a short time. Then, the data fusion is performed by the joint Kalman filter, least-squares fusion, and extended Kalman filter, respectively. During the experimental process, the system is proved to have good robustness, that is, in the case of individual sensor failure, the attitude acquisition section still obtains accurate attitude information of the UAV. The attitude reference system of UAV is realized. With the help of multi-sensor/information fusion technology, the attitude of the UAV is better handled, and its flight stability is improved.

Mobile node localization method based on KF-LSSVR algorithm

As the mobile node localization algorithm in three-dimensional environment cannot meet the demand of actual application, a hybrid mobile node localization algorithm for a wireless sensor network (WSN) in three-dimensional environment is proposed in this paper, which is based on the least squares support vector regression (LSSVR) and Kalman filter (KF). The proposed algorithm firstly constructs the LSSVR localization model by sampling measurement area and training sample sets. Then, the KF model is used to iterate and correct the measured distance in order to obtain the distance between the unknown node and each anchor node. Finally, the LSSVR localization model is employed to obtain the estimated location of the unknown node. The experiments were conducted and the experimental results were analyzed according to ranging errors, anchor node density, communication radius, moving speed, and node localization errors. Simulation results show that the proposed algorithm using a joint KF and LSSVR algorithm is superior to the KF algorithm and the LSSVR algorithm, and it can reduce the localization errors and improve the localization accuracy.

Online natural gradient as a Kalman filter

We cast Amari’s natural gradient in statistical learning as a specific case of Kalman filtering. Namely, applying an extended Kalman filter to estimate a fixed unknown parameter of a probabilistic model from a series of observations, is rigorously equivalent to estimating this parameter via an online stochastic natural gradient descent on the log-likelihood of the observations. In the i.i.d. case, this relation is a consequence of the “information filter” phrasing of the extended Kalman filter. In the recurrent (state space, non-i.i.d.) case, we prove that the joint Kalman filter over states and parameters is a natural gradient on top of real-time recurrent learning (RTRL), a classical algorithm to train recurrent models. This exact algebraic correspondence provides relevant interpretations for natural gradient hyperparameters such as learning rates or initialization and regularization of the Fisher information matrix.

Online Model Adaption of Reduced Order Models for Fluid Flows

In this study an online adaption of reduced order models (ROM) for fluid flows is presented. Often ROMs are stabilized and tuned by use of an eddy viscosity which is identified in the offline phase. As novelty the authors estimate the eddy viscosity in the online phase by treating the viscosity as an uncertain parameter in the framework of an extended joint Kalman filter. The mode dependency of the eddy viscosity is estimated at the same time as the eddy viscosity itself using a generalization of typical models for the eddy viscosity of fluid flows. Effectiveness of the proposed method is shown using the Burgers Equation and the incompressible two dimensional Navier-Stokes-Equations.

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.