Adaptive Kalman Filter Research Articles

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method.

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10-3 to 1.0132 × 10-4, the factor of bias instability (B) reduces from 1.53 × 10-2 to 1 × 10-3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10-4 to 7.2110 × 10-6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

Research on a High-Precision State-of-Charge Estimation Method Based on Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Applied to LiFePO4 Battery

The state-of-charge (SOC) estimation accuracy is closely associated with the estimation method and the battery parameter identification performance. The battery parameter identification method based on forgetting factor recursive least squares (FFRLS) has the advantages of high parameter identification accuracy and fast dynamic response speed. On this basis, the performance of two SOC estimation methods, the extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) are compared and studied. The results show that AEKF has better steady-state and dynamic SOC estimation performance, but the estimation accuracy and dynamic response performance are still not objective. To further improve the performance of SOC estimation, a joint SOC estimation method based on FFRLS-AEKF is proposed, and the SOC estimation experimental results with FFRLS-AEKF and AEKF are conducted. The experimental results show that the proposed joint SOC estimation method based on FFRLS-AEKF has a better steady-state and dynamic performance of SOC estimation. The maximum absolute error of the proposed algorithm is 4.97%. As the battery working time increases, the SOC estimation accuracy continues to converge to the true value, and the average absolute error is reduced to 2.5%. The proposed method and theoretical analysis are proven to be correct and feasible.

Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter

The effectiveness of gas flow control will significantly affect the output performance of the PEMFC, however, the gas state inside the cathode is difficult to observe due to the tightness and safety requirements. To achieve effective control of PEMFC in the presence of parameter bias, an improved active disturbance rejection control (IADRC) strategy based on the adaptive unscented Kalman filter (AUKF) is proposed in this paper. A dynamic model of the air supply system with seven state variables was developed, capable of describing the non-linear characteristics of the system, and a semi-empirical air compressor model was designed to match the actual operating characteristics based on the experimental data set. Based on the standard UKF algorithm, an adaptive process was designed using the moving window method to dynamically adjust the noise characteristics to reduce the state estimation bias caused by changes in system structural parameters during service. Finally, the algorithmic structure of the ADRC was optimized to achieve an accurate estimation of the total system perturbation using cascaded extended state observers (ESO), thus achieving a control effect with the advantages of less overshoot and response speed based on fewer tuning parameters. Simulation results show that the proposed strategy can effectively improve the system response and robustness to structural parameters, and has great control effects under real road conditions.

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.

A Modified Strong Tracking Adaptive Kalman Filter for Precision Clock Synchronization System

In order to reduce the clock offset caused by network delay jitter, network asymmetry, clock drift and clock frequency difference in the clock synchronization process of IEEE1588, a modified Strong Tracking Adaptive Kalman Filter (MSTAKF) algorithm was proposed. The Kalman filter model established by the algorithm is based on clock offset, network delay, frequency deviation and asymmetry ratio. The filter includes the modified strong tracking algorithm based on fuzzy logic and hypothesis test proposed in this paper, which can effectively eliminate the influence of outliers, and finally make the model tracking effect better. In addition, the modified Sage‐Husa algorithm based on Cholesky decomposition is also included, which makes the measurement noise more stable and ensures the positivity of the measurement noise covariance. The experimental results show that the proposed MSTAKF is smoother than Karman filter (KF), Sage‐Husa adaptive Kalman filter (AKF) and strong tracking Sage‐Husa adaptive Kalman filter (STAKF). Compared with these filter algorithms after testing in the LAN environment, the precision improvement and accuracy rate are significantly improved. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator

This paper puts forward a novel deep reinforcement learning control using deep deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking and vibration suppression problem of rotary flexible link (RFL) manipulator. Specifically, this study attempts to address the continuous action space DRLC problem through DDPG algorithm and presents a Lyapunov function based reward shaping approach for guaranteed deep reinforcement learning (DRL) convergence and enhanced speed of training. The proposed approach synthesizes the hard and soft constraints of the flexible manipulator as a constrained Markov decision problem (MDP) and evaluates the performance of DRLC-DDPG framework through hardware in loop (HIL) testing to realize precise servo tracking and suppressed vibration of the flexible manipulator. For identifying the dynamical model of the RFL, an empirical Auto-Regressive eXogenous (ARX) model using the closed loop identification technique is built. Moreover, to extract the true states (servo angle and deflection angle) from the actual measurements, which typically have the influence of sensor noise, an adaptive Kalman filter (AKF) is augmented with the DRLC scheme. The experimental results of DRLC-DDPG scheme compared with those of the model predictive control (MPC) for several test cases reveal that the proposed scheme is superior to MPC both in terms of trajectory tracking and robustness against the external disturbances and model uncertainty.

Robust adaptive quadrotor position tracking control for uncertain and fault conditions

The combination of nonlinear indirect and direct adaptive control with an active fault-tolerant framework is proposed in this paper for quadrotor position tracking control under uncertain and fault conditions. An inner loop direct model reference adaptive controller generates the required force while moving along the reference trajectory, while nonlinear indirect adaptive controller maintains the required attitude angles. Furthermore, for uncertain conditions, our proposed framework provides significantly stable characteristics. Otherwise, when a fault occurs at actuators or sensors, the quadrotor vehicle cannot guarantee global asymptotic stability. This study contributes to an active fault-tolerant control strategy for solving the complex position tracking problem using the adaptive two-stage Kalman filter (ATSKF). Based on the fault information, a fault compensation term is added to the control law to improve convergence and system robustness in the presence of uncertain and fault conditions. Finally, simulation results show satisfactory performance for quadrotor vehicle position tracking, even with actuator faults and uncertainties.

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

SummaryAccurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

Approximate Gaussian variance inference for state‐space models

SummaryState‐space models require an accurate knowledge of the process error () and measurement error () covariance matrices for exact state estimation. Even though the matrix can be, in many situations, considered to be known from the measuring instrument specifications, it is still a challenge to infer the matrix online while providing reliable estimates along with a low computational cost. In this article, we propose an analytically tractable online Bayesian inference method for inferring the matrix in state‐space models. We refer to this method as approximate Gaussian variance inference (AGVI) using which we are able to treat the error variance and covariance terms in the full matrix as Gaussian hidden states and infer them simultaneously with the other hidden states in a closed‐form manner. The two case studies show that the method is able to provide statistically consistent estimates for the mean and uncertainties of the error variance terms for univariate and multivariate cases. The method also exceeds the performance of the existing adaptive Kalman filter methods both in terms of accuracy and computational efficiency.

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises.

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model's high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target's maneuvering turn. Second, an improved Sage-Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

Research on guidance information extraction technology of strap-down seeker based on adaptive cubature Kalman filter

In comparison to the traditional platform seeker, strap-down seeker, which is firmly connected with rocket-propelled vehicle, has a lot of strength in a smaller size, a simpler structure, and a lower cost. However, because it is fixed on the rocket-propelled vehicle, inertial line-of-sight(LOS) angular velocity demanded by guidance system could not be directly obtained during the rocket-propelled vehicle flight. In this paper, according to the coordinate transformation relationship, the inertial LOS angular velocity decoupling model is established. Aiming at measurement error characteristics of a seeker, a guidance information extraction algorithm which is on basis of the adaptive cubature Kalman filter is studied, and the digital simulation experiment is carried out in combination with the engineering practice. The simulation data demonstrate that the suggested algorithm has a significant effect on improving the filtering estimation stability and accuracy of guidance information extraction and guaranteeing the accuracy of rocket-propelled vehicle hits.

A Pearson-Type VII Distribution With Adaptive Parameters Selection-Based Interacting Multiple Model Kalman Filter

Existing robust filters under generalized nonstationary noises conditions are difficult to choose suitable prior parameters, this brief proposed a Pearson-type VII distribution with adaptive parameters selection based interacting multiple model (IMM) Kalman filter (IMMPVII KF). In the model conditional filtering process, both the one-step prediction and the measurement likelihood are modeled as Pearson-type VII distributions. They are decomposed into Gaussian-Gamma Hierarchies (GGH), which are then matched to the time-varying heavy-tailed properties of the noises by pre-selecting the sets of shape and rate parameters and the variational Bayesian (VB) technique. Finally, a new model probability update method for filter under non-Gaussian conditions is derived. Simulation results show that the filter proposed in this brief has better robustness and adaptability to generalized non-stationary noises than the existing filters.

Modified Strong Tracking Slide Window Variational Adaptive Kalman Filter With Unknown Noise Statistics

The filter performance will be degraded in the measurements with time-varying and unknown noise statistics. To combat the above challenges, a modified strong tracking slide window variational adaptive Kalman filter algorithm is proposed in this article. Firstly, the multiple fading factors are integrated into the proposed algorithm to adjust the error covariance. Next, an improved adaptive slide window method is designed for variational Bayesian Kalman filtering by adaptively adjusting the slide window size and correcting the previous state according to the later state, which improves the estimation accuracy and computational efficiency. Finally, the inverse Wishart distribution is considered for modeling process and measurement noise, and the state vector, as well as noise statistics, are inferred via the variational Bayesian technique without prior noise covariance information. Simulation results demonstrate that the proposed filter algorithm is more robust than existing filters in counteracting measurement and process noise uncertainties.

The effect of the peristimulus alpha phase on visual perception through real-time phase-locked stimulus presentation.

The alpha phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering. This information guided online presentations of masked stimuli that were phased-locked to different stages of the alpha cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus alpha phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between alpha phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the alpha phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.Significance statementAlpha activity, a widely observed neural phenomenon, is postulated to be essential for organizing visual perception. However, our previous understanding of the functional relevance of the alpha phase is primarily inferred from the prestimulus or externally entrained phase. This study monitors real-time phase activity and presents stimuli that are phased-locked to different stages of the alpha cycle. This new approach allows us to investigate whether and how the alpha phases, during which stimuli are concurrently presented, directly lead to behavioral and neural changes in perception. Our evidence suggests that the extent to which the alpha phases affect perception depends on an early interaction between the phase and incoming stimuli, which is involved in shaping the perceptual fates of the stimuli.

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.

Hybrid SOC and SOH estimation method with improved noise immunity and computational efficiency in hybrid railroad propulsion system

The fuel-cell and lithium-ion battery (LIB) hybrid railroad propulsion system (HRPS) technologies are actively being researched to reduce greenhouse gas (GHG) emissions. It is becoming increasingly important to accurately estimate the state-of-charge (SOC) and state-of-health (SOH) to enhance the safety and efficiency of the system. For a real-time embedded system, computational efficiency and the effect of noise are crucial challenges for SOC and SOH estimation. To overcome these problems, a method is required that improves computational efficiency and minimizes the effect of noise.To reduce computational cost and the effect of noise, the adaptive extended Kalman filter (AEKF) and specific partial capacity with single forgetting factor recursive least square (SPCSFFRLS) methods were combined to estimate SOC and SOH. In AEKF-based SOC estimation, the state and measurement noise covariance matrices were corrected by the mean square error (MSE) of the difference between measured voltage and modeled voltage. Thus, AEKF is effective in conquering measurement noise. The battery capacity was obtained by the SPCSFFRLS using station-to-station operation data. To calculate true specific partial capacity (SPC) without noise, the partial capacity method and SFFRLS were used to update the battery capacity. However, as the current accumulation period becomes longer, an accumulation error due to measurement noise may occur. Therefore, a specific period was analyzed to reduce accumulation error caused by measurement noise in the HRPS load profile. Finally, SOC and SOH co-estimation was demonstrated for a single cell and a parallel-connected battery pack (1S18P). As a result, it was found that the proposed method can reduce the computational time by about 33.0 % average while keeping the SOC and SOH estimation accuracy compared to DEKF. Thus, the proposed method is suitable for real-time embedded system such as HRPS to estimate SOC and SOH.

An Internet of Electronic-Visual Things Indoor Localization System Using Adaptive Kalman Filter

In recent years, various indoor social security emergencies have brought major changes to people’s lives. However, the state-of-art surveillance technology achieving object tracking alone is insufficient through a single visual tracking method and may cause inaccurate tracking (such as burred faces, loss target, and occlusion) and is time-consuming for a long video sequence. Accurate tracking and localization systems are essential for developing indoor localization systems. This paper designs a fusing communication system called the Internet of Electronic-Visual Things Localization system (IoEVT), which simultaneously tracks targets assisting with an Adaptive Kalman Filter (AKF). The data fusion indoor localization approach using AKF fuses multi-data from electronic (E) and visual (V) data, such as WiFi and cameras. The fusing method significantly improves EV data sensing abilities for solving occluded visual information when only using single V data in occlusion scenarios and reduces the localization error rate. In addition, our real-world experimental validation shows that the proposed IoEVT system achieves better accuracy, scalability, and robustness with low network bandwidth consumption compared with the single E or V data in many scenarios, such as occlusion and occlusion-free.