Fusion Estimation Method Research Articles

IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises

In recent years, multisensor fusion for state estimation has gained considerable attention. The effectiveness of the optimal fusion estimation method heavily relies on the correlation among sensor measurement noises. To enhance estimate fusion performance by mining unknown correlation in the data, this paper introduces a novel multisensor fusion approach using an information filtering neural network (IFNet) for discrete-time nonlinear state space models with cross-correlated measurement noises. The method presents three notable advantages: First, it offers a data-driven perspective to tackle uncertain correlation in multisensor estimate fusion while preserving the interpretability of the information filtering. Second, by harnessing the RNN’s capability to manage data streams, it can dynamically update the fusion weights between sensors to improve fusion accuracy. Third, this method has a lower complexity than the state-of-the-art KalmanNet measurement fusion method when dealing with the fusion problem involving a large number of sensors. Numerical simulations demonstrate that IFNet exhibits better fusion accuracy than traditional filtering methods and KalmanNet fusion filtering when correlation among measurement noises is unknown.

State of health estimation for lithium-ion batteries based on multi-scale frequency feature and time domain feature fusion method

Abstract Accurately estimating the state of health (SOH) of lithium-ion batteries is important for improving battery safety performance. The single time-domain feature extraction is hard to efficiently extract discriminative features from strongly nonlinear coupled data, leading to difficulties in accurately estimating the battery SOH. To this end, this paper proposes a multi-scale frequency domain feature and time domain feature fusion method for SOH estimation of lithium-ion batteries based on the Transformer model. Firstly, the voltage, current, temperature and time information of the battery are extracted as time domain features; secondly, the battery signal is processed by a multi-scale filter bank based on Mel-frequency cepstral coefficients (MFCC) to obtain the multi-scale frequency domain features; then, a parallel focusing network (PFN) is designed to fuse the time domain features with the frequency domain features, which yields low-coupling complementary discriminative features; finally, constructing the SOH estimation mechanism based on the Transformer deep network model. The algorithm is validated by NASA and Oxford datasets, the mean's absolute error (MAE) and root mean square error (RMSE) are as low as 0.06% and 0.23%, respectively.

Thrust online fusion estimation of high-flow dual variable cycle engine

For the aeroengines, thrust is a crucial performance parameter which is closely related to the mission accessibility. Accurate thrust control is conducive to excavate the potential of the variable cycle engine (VCE) in different operation modes, which meets the requirements of future engine control system. However, the thrust is unmeasured in flight. Besides, the traditional thrust estimation method is difficult to meet the wide-area thrust control requirements due to the strong nonlinearity and large flight envelope for a neoconfiguration VCE of the high-flow triple-bypass, named high-flow dual variable cycle engine (HDVCE). This paper proposes a novel online fusion thrust estimation method mainly under single operation mode for the HDVCE. The problem of individual engine differences is also focused, and the combination of model and data-driven is to generate the thrust estimation with fusion strategy. At first, a multi-combustion chamber coupled dynamic model is established for the HDVCE, and an adaptive network is used for model optimization to improve the model accuracy. On this basis, an EKF-based thrust estimator is built. Secondly, a data-driven thrust estimator is pre-trained by a stacked autoencoder (SAE) and then tuned by back propagation (BP) neural network. Subsequently, the thrust integration is incorporated based on estimation results from the EKF-based and data-driven one using Euclidean distance fusion strategy. Simulation results show that the fusion estimation method proposed in this paper, on the one hand, reduces the effect of model instability on EKF. On the other hand, it ensures the estimation accuracy of SAE-BP far from the training point. The comparisons also reveal that the fusion one produces more than 97% in thrust estimation accuracy and is better than the remaining ones, which is promising for thrust control applications.

Watermarking-based remote secure sequential fusion estimation under the event-triggered mechanism

This paper investigates the problem of remote sequential fusion estimation using event-triggered communication mechanism in cyber–physical systems with deception attack interference. The watermarking defense strategy is used to encrypt and decrypt the data transmitted by sensors, which is convenient for the χ2 detector to detect deception attacks. The effectiveness of the χ2 detector is verified for three different attack scenarios. In addition, an event-triggered mechanism is designed based on the prediction error variance, which greatly saves the network resource while guaranteeing the accuracy of sequential fusion estimation. Finally, simulation results are given to illustrate the effectiveness of the proposed sequential fusion estimation method.

Secure fusion estimation against FDI sensor attacks in cyber–physical systems

This paper is concerned with the secure fusion estimation problem in cyber–physical systems (CPSs), where false data injection attack (FDIA) is able to falsify measurement signals transmitted over network. In this paper, we consider that adversaries are not able to attack all measurements, i.e., several measurements remain not being attacked. Under this condition, local augmented systems including FDIA signals are respectively constructed with un-attacked measurements. For each augmented system, we design the joint Kalman fusion estimator, under the linear minimum variance sense, to simultaneously estimate FDIA and system states, which is fulfilled by introducing a compensation factor. Finally, a simulation example is given to verify effectiveness and advantages of the secure fusion estimation method.

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors.

Relative localization (RL) and circumnavigation is a highly challenging problem that is crucial for the safe flight of multi-UAVs (multiple unmanned aerial vehicles). Most methods depend on some external infrastructure for positioning. However, in some complex environments such as forests, it is difficult to set up such infrastructures. In this paper, an approach to infrastructure-free RL estimations of multi-UAVs is investigated for circumnavigating a slowly drifting UGV0 (unmanned ground vehicle 0), where UGV0 serves as the RL and circumnavigation target. Firstly, a discrete-time direct RL estimator is proposed to ascertain the coordinates of each UAV relative to the UGV0 based on intelligent sensing. Secondly, an RL fusion estimation method is proposed to obtain the final estimate of UGV0. Thirdly, an integrated estimation control scheme is also proposed for the application of the RL fusion estimation method to circumnavigation. The convergence and the performance are analyzed. The simulation results validate the effectiveness of the proposed algorithm for RL fusion estimations and of the integrated scheme.

Sequential Fusion Estimation for Multirate Complex Networks With Uniform Quantization: A Zonotopic Set-Membership Approach.

In this article, the sequential fusion estimation problem is investigated for multirate complex networks (MRCNs) with uniformly quantized measurements. The process and measurement noises, which are unknown-yet-bounded (UYB), are restrained into a family of zonotopes, and the multiple sensors are allowed to have different sampling periods. To facilitate digital transmissions, the sensor measurements are uniformly quantized before being sent to the remote estimator. The purpose of this article is to design a sequential set-membership estimator such that, in the simultaneous presence of UYB noises, multirate samplings, and uniform quantization effects, the estimation error (after each measurement update) is confined to a zonotope with minimum F -radius at each time instant. By introducing certain virtual measurements, the MRCNs are first transformed into single-rate ones exhibiting a switching phenomenon. Then, by utilizing the properties of zonotopes, the desired zonotopes are derived, which contain the estimation error dynamics after each measurement update. Subsequently, the gain matrices of the sequential estimator are derived by minimizing the F -radii of these zonotopes, and the uniform boundedness is analyzed for the F -radius of the zonotope containing the estimation error after all measurement updates. Furthermore, sufficient conditions are derived to ensure the existence of the desired uniform upper/lower bounds. Finally, an illustrated example is proposed to show the effectiveness of the proposed sequential fusion estimation method.

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

Sideslip Angle Fusion Estimation Method of Three-Axis Autonomous Vehicle Based on Composite Model and Adaptive Cubature Kalman Filter

A sideslip angle fusion estimation strategy of three-axis vehicle based on adaptive cubature Kalman filter is investigated in this paper. According to the dynamics model, kinematics model of the three-axis vehicle, and considering the influence of tire nonlinearity, the vehicle state estimators under different conditions are designed by using the adaptive cubature Kalman filter algorithm. The dynamic-model-based estimator with linear tire model, dynamic-model-based estimator with nonlinear tire model, and kinematical-model-based estimator are proposed, then, according to the application characteristics of different estimators, a fusion estimation strategy of vehicle sideslip angle based on adaptive fuzzy weight controllers is designed, so as to improve the overall estimation accuracy by integrating the advantages of the three estimators. The simulation and experimental results show that the presented fusion estimation strategy can effectively improve the estimation accuracy of vehicle sideslip angle, and the comprehensive estimation accuracy reaches 94.37%.

A method for localization TDOA estimation based on signal‐level fusion and analysis

AbstractPassive localization using time difference of arrival (TDOA) is one of the common methods for radiated source target localization, and its localization performance depends on the accuracy of TDOA estimation. In many localization scenarios, the signals of target are intricate and often contain multiple ones, so how to effectively extract the time difference parameters from them becomes a difficult problem. This letter proposes a signal‐level fusion method for TDOA estimation to address this problem. At the receiver terminal, multiple signals are fused to obtain their cross‐ambiguity function, so as to estimate the TDOA value for target localization. The effect of fused signals on the accuracy of TDOA estimation is analyzed and the concept of frequency depression is proposed. Simulation experiments verify that the estimation method with signal‐level fusion gets a higher accuracy of the TDOA value and a smaller position error in target localization.

Zonotopic non-fragile set-membership fusion estimation for nonlinear systems under sensor resolution effects: Boundedness and monotonicity

In this paper, the zonotopic non-fragile set-membership fusion estimation problem is investigated for multi-sensor nonlinear systems under sensor resolution effects. The sensor resolutions, which characterize the smallest change of signals that can be detected by sensors, are considered to reflect the engineering practice. A sequential fusion estimator, which takes the possible estimator gain variations (PEGVs) into account, is constructed to estimate the system state. Based on the mean value theorem and the first-order Taylor expansion, a novel method is first proposed to derive a zonotope containing the one-step prediction error, and such a method is proven to be beneficial in improving the prediction accuracy. Then, a PEGV-dependent zonotope is derived to enclose the estimation error after each measurement update, and the estimator parameter is designed to minimize a certain upper bound of the F-radius of the derived zonotope. Subsequently, in order to acquire bounds of the system states, a PEGV-independent zonotope is obtained at each time instant to enclose the estimation error after the measurement update. Moreover, the monotonicity of the F-radius of the PEGV-independent zonotope is analyzed with regard to, respectively, the corresponding sensor resolution and the PEGV. Finally, two numerical examples are provided to show the effectiveness of the proposed fusion estimation method.

An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries

The state of charge estimation of lithium-ion batteries in electric vehicles is critical in battery management systems. Using a single model and algorithm for state of charge estimation has some limitations, therefore fusion algorithms are investigated by researchers. However, among many fusion methods, there are no basis for judgement concerning which models and algorithms to be chosen for fusion in different discharge intervals in order to obtain the desired results. To address this problem, this study proposes a three-interval fusion method for state of charge estimation based on fitness values. Firstly, three different types of equivalent circuit models and six filtering algorithms are built in this study. Next, the whole state of charge is divided into three intervals in discharging process and the estimation error characteristics of each interval are extracted. Then, assessment of model-algorithm fits through fitness values. Finally, the combinations of better fitness values in each interval are selected and the three-interval fusion method for estimation of state of charge is achieved by adaptive weighted average. The results show that the maximum absolute error of the three-interval fusion method is less than 0.5 % and the accuracy is improved by 40 % compared to other fusion methods.

A fusion method for capacity estimation of fast-charging lithium-ion battery under varied degradation mode

Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most of the existing works are proposed for batteries charged under the 1C rate. However, incorrect fast-charging strategies may result in Li plating leading to battery capacity aging rapidly and degradation mode variation. Moreover, unprecise sampling by BMS increases the difficulty of accurately estimating battery capacity in such scenarios. Thus, a fusion prognostic method based on ensemble learning is proposed for the above issues in fast-charging batteries. Firstly, measurement-based health features are extracted from the portion charging phase. Then, the accuracy of the validation dataset-based strategy is proposed to achieve time-varying weight allocation. Finally, a fusion prognostic model is constructed based on the above weight allocation strategy and health features. The effectiveness of the proposed fusion method is verified by a self-design fast-charging battery dataset that maintains stable and reliable performance under sparse sampling and degradation mode variation conditions. In addition, the robustness and adaptability are validated by the comparison experiments with traditional ensemble learning-based methods, in which accuracy is improved by 27.5% minimally.

A Soft Sensor for Estimating Tire Cornering Properties for Intelligent Tires

Intelligent tire systems are promising solutions for achieving precise vehicle state estimations, localization, and motion control in the context of autonomous driving. Tire cornering properties, namely, lateral force, aligning moment, and pneumatic trail, are crucial factors that should be accurately estimated for vehicle dynamics control purposes. In this work, a soft sensor for estimating tire cornering properties based on intelligent tire and machine learning is developed. The intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread, which provides acceleration measurements from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$</tex-math> </inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$</tex-math> </inline-formula> directions. Partial least squares and variable importance in the projection scores (PLS-VIP) are used in the feature extraction of the acceleration signals over the contact patch. A Gaussian process regression (GPR) model is trained to predict the cornering properties with confidence intervals under different input conditions. Based on the variances in the GPR predictions and minimum mean-square error criterion, a data fusion method for pneumatic trail estimation is proposed. It is demonstrated that the developed GPR models for cornering properties and the data fusion method for pneumatic trail estimation have satisfactory accuracy and reliability. The experimental results show that the soft sensor proposed in this work is a strong candidate for further applications in the development of vehicle state estimation and control algorithms.

A robust fusion bus frequency estimation method to improve frequency oscillation damping in power systems

AbstractThis paper proposes a new robust method for accurately and reliably estimating remote bus frequencies in a power system. Two different measurement sources for the remote bus frequency are considered, that is, the ideal Frequency Divider (FD) and the Synchronous Reference Frame Phase Locked Loops (SRF‐PLLs). Each measurement signal encounters different uncertainties and data quality issues. In this paper, both data sources are employed and fused together to better estimate the remote bus frequencies. To this end, the model structure of the power system is selected and an Unscented Kalman Filter (UKF) is utilized together with a fusion covariance intersection method to enhance the accuracy of the estimated bus frequency. Since the fusion estimation method fails in case of quality issues on both channels, a robust Generalized Maximum‐likelihood UKF (GM‐UKF) using a novel outlier detection criteria is developed. The impact of the resulting robust fusion filter on the estimation of the remote bus frequencies and on the performance of WAPSS, which makes use of the estimated frequencies as the feedback signal, is examined via simulations. The results demonstrate the excellent performance and reliability of the proposed method in dealing with noise filtering and outlier suppression while ensuring a high statistical efficiency.

Online fusion estimation method for state of charge and state of health in lithium battery storage systems

To ensure the safe and reliable operation of Li-ion battery energy storage systems, it is important to diagnose the operational status and aging degree of the batteries. In this study, an online fusion estimation method based on back propagation neural network and genetic algorithm (BP-GA) is used for estimating the state of charge (SoC) and state of health (SoH) of Li-ion batteries. First, the effective features of SoC and SoH of Li-ion batteries during charging and discharging are analyzed, and the relevant features are extracted. Subsequently, a conventional back propagation neural network (BPNN) for SoC and SoH estimation is described. The extracted feature quantities are then used as inputs to the neural network for the estimation of the SoC and SoH of Li-ion batteries. A comparison of the SoC and SoH estimation results using, respectively, a single BPNN and a fusion algorithm using a genetic algorithm (GA) to optimize the BPNN shows that the accuracy and efficiency of the estimation results using BP-GA are significantly higher than that of the BPNN. The experimental component of this paper is a comparison experiment using the Panasonic 18650PF and the real-world data from NASA. The results show that the feature extraction operation is relatively easy to perform and combined with the neural network method used in this paper can have good accuracy, reduce the complexity of the algorithm, and improve the detection efficiency.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems

This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.

Adaptive Active Fusion of Camera and Single-Point LiDAR for Depth Estimation

Depth sensing is an important problem in many applications such as autonomous driving, robotics, and automation. This paper presents an adaptive active fusion method for scene depth estimation by using an RGB camera and a single-point LiDAR. An active scanning mechanism is proposed to guide laser scanning based on critical visual and saliency features, and the convolutional spatial propagation network (CSPN) is designed to generate and refine full depth map from the sparse depth scans. The active scanning mechanism generates a depth mask by using log-spectrum saliency detection, Canny edge detection, and uniform sampling, which indicate critical regions that require a high resolution of laser scanning. To reconstruct a full depth map, the designed CSPN network extracts affinity matrices from the sparse depth scans, while reserving global spatial information in the RGB images. The performance of proposed method was evaluated and compared with the state-of-the-art methods on the NYUv2 dataset, and the experiment demonstrated its outperformance in reconstruction accuracy and robustness to measurement noise. The proposed method was also evaluated in real-world scenarios.

Practical battery State of Health estimation using data-driven multi-model fusion

Due to dynamic vehicle operating conditions, random user behaviors, and cell-to-cell variations, accurately estimating the battery state of health (SoH) is challenging. This paper proposes a data-driven multi-model fusion method for battery capacity estimation under arbitrary usage profiles. Six feasible and mutually excluded scenarios are meticulously categorized to cover all operating conditions. Four machine learning (ML) algorithms are individually trained using time-series data to estimate the current time step battery capacity. Additionally, a prediction model based on the histogram data is adopted from previous work to predict the next step capacity value. Then, a Kalman filter (KF) is applied to fuse all the estimation and prediction results systematically. The developed method has been demonstrated on cells operated under diverse profiles to verify its effectiveness and practicability.