Unscented Particle Filter Algorithm Research Articles

Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering

To address the limitations in the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, stemming from model accuracy, particle degradation, and insufficient diversity in the particle filter (PF) algorithm, this paper proposes a battery RUL prediction method utilizing a randomly perturbed unscented particle filter (RP-UPF) algorithm, based on the constructed battery capacity degradation model. The method utilizes evaluation metrics adjusted R-squared (Radj2) and the Akaike Information Criterion (AIC) to select the battery capacity decline model C5 with a higher goodness of fit. The initial values for constructing the C5 model are obtained using the relevance vector machine (RVM) and nonlinear least squares methods. Based on the constructed battery capacity decline model C5, the RP-UPF algorithm is employed to estimate the posterior parameters and iteratively approach the true battery capacity decline curve, thereby predicting the battery’s RUL. The research results indicate that, using battery B0005 as an example and starting the prediction from the 50th cycle, the RUL prediction results obtained with the RP-UPF algorithm demonstrate reductions in absolute error, relative error, and probability density function (PDF) width of 2%, 2.71%, and 10%, respectively, compared to the PF algorithm. Similar conclusions were drawn for batteries B0006 and B0018. Under the constructed battery capacity degradation model C5, the RP-UPF algorithm shows higher prediction accuracy for battery RUL and a narrower PDF range compared to the PF algorithm. This approach effectively addresses the issue of particle weight degradation in the PF algorithm, providing a more valuable reference for battery RUL prediction.

SFDM: A time-synchronization-free detection mechanism for mobile underwater acoustic sensor networks

Detection and localization are important applications of underwater acoustic sensor networks, and the use of passive listening arrays composed of multiple underwater unmanned platforms to achieve passive detection and localization of targets has been a hot research topic in recent years. However, most schemes based on mobile listening arrays only aim to improve localization accuracy, with less consideration of the high communication and time overhead of formation maintenance and the high cost of time synchronization among nodes. To reduce the cost while ensuring localization accuracy, this paper proposes a time-synchronization-free detection mechanism for mobile underwater acoustic sensor arrays. Initially, the nodes cruise underwater until they passively detect a target signal. Subsequently, a pseudo-synchronization and rough self-calibration process is triggered among the nodes, which relies on the communication among the nodes. The communication stage is based on a time-synchronization-free detection protocol. An improved sound speed stratification effect compensation algorithm and an unscented particle filter algorithm are proposed to improve the calibration accuracy of the array. Finally, the Time Difference of Arrival method is utilized to locate the target position. Simulation and sea trial results confirm the feasibility and accuracy of the proposed mechanism. In addition, the mechanism is effective in reducing hardware costs.

IMUPF-BIN: A new autonomous search method for radioactive sources

To solve the problem of mobile robots autonomously searching for unknown radioactive sources, this paper proposes an automatic source search strategy, abbreviated as IMUPF-BIN: the location of the radioactive source is estimated by improved unscented particle filtering (IMUPF) algorithm, and the search path is predicted and planned by bioinspired neural network (BIN) model. First, the search environment is expressed as an occupied grid map, and the BIN model is combined with the grid occupied map to obtain the environment information in the search process. Then, the unscented particle filter (UPF) which is used for source parameter estimation is improved by introducing the idea of linear optimization to improve the efficiency of particle usage and the diversity of particles. Further, the BIN model is introduced to reduce the influence of obstacles on the movement of the robot, thereby improving the search accuracy and efficiency of radioactive source. After the robot detects the radiation count value, the parameter information of the source is estimated through the IMUPF algorithm. By using the activity value of the BIN model and distributed model predictive control (DMPC) to plan the path, while continuously iterating the search process, the location of the radioactive source is finally determined. The simulation results show that in without obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 85.6% and 52.8% compared to the standard particle filtering (PF) and UPF algorithm respectively, and the search time of the IMUPF-BIN algorithm is reduced by 75.9% compared to the gradient search (GS) algorithm. In obstacles environment, the localization accuracy of the IMUPF algorithm is improved by 80.8% and 35.9% compared to the PF and UPF algorithms respectively. In addition, the GS algorithm fails to search for the radioactive source due to the shield of obstacles, while the IMUPF-BIN algorithm can successfully search for radioactive source in a shorter search time.

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety. However, due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles, the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation. This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm, which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle. In the dual unscented particle filter algorithm, two unscented particle filter transfer information to each other, observe the vehicle state information and the tire force parameter information of the four wheels respectively, which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving. The performance with the dual unscented particle filter algorithm, which is analyzed in terms of the time-average square error, is superior of the unscented Kalman filter algorithm. The effectiveness of the algorithm is further verified by driving simulator test. In this paper, a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Particle filter algorithms for on-line prediction of transient heat flux of participating medium

The particle filter (PF) and corresponding improved algorithm (unscented particle filter (UPF)) are introduced to solve the on-line prediction of transient heat flux (q(t)) on the boundary of participating medium for the first time. The temperature-dependent thermophysical properties of the medium result in a highly nonlinear heat transfer system. The graded index medium (caused by the temperature-varying refractive index) is also taken into account, in which the radiation transfer is more complex than that in the uniform refractive index medium. The finite volume method is employed to address the transient coupled radiative and conductive heat transfer in medium. The impacts of particle number, process error covariance, measurement error covariance, sampling time interval, forms of heat flux and measurement errors on prediction results are researched in detail. The UPF algorithm, in which unscented Kalman filter technique is applied to pick an appropriate proposal density function to ameliorate the degradation phenomenon in original PF algorithm, outperforms the PF algorithm in terms of stability and accuracy of estimation results. All the prediction results demonstrate that UPF method is effective and reliable to solve the real-time reconstruction of q(t) even with measurement errors.

A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

The unscented particle filter (UPF) combines the advantages of both the unscented Kalman filter and particle filter (PF) algorithms. However, during the fusion of multi-source information for UAV integrated navigation, the UPF may encounter problems related to measurement biases and particle depletion. These issues can lead to algorithm results that are unsatisfactory or divergent. To address these challenges, a novel approach called the consider unscented particle filter with genetic algorithm (GA-CUPF) is proposed. The GA-CUPF algorithm incorporates covariance and co-covariance of biases into the state estimation covariance through the ‘consider’ method employed by the CUPF, and it should be noted that the biases are not estimated in this process. Moreover, the GA-CUPF algorithm enhances the importance probability density of particles to avoid the degeneracy of PF algorithm and improves particle diversity through the application of selection, crossover, and mutation methods in GA. Simulation results demonstrate that the proposed GA-CUPF algorithm can effectively mitigate the adverse effects of measurement biases and particle depletion that the UPF algorithm faces in the UAV navigation system. Additionally, it significantly improves the accuracy of UAV integrated navigation.

Adaptive SLAM Methodology Based on Simulated Annealing Particle Swarm Optimization for AUV Navigation

Simultaneous localization and mapping (SLAM) is crucial and challenging for autonomous underwater vehicle (AUV) autonomous navigation in complex and uncertain ocean environments. However, inaccurate time-varying observation noise parameters may lead to filtering divergence and poor mapping accuracy. In addition, particles are easily trapped in local extrema during the resampling, which may lead to inaccurate state estimation. In this paper, we propose an innovative simulated annealing particle swarm optimization-adaptive unscented FastSLAM (SAPSO-AUFastSLAM) algorithm. To cope with the unknown observation noise, the maximum a posteriori probability estimation algorithm is introduced into SLAM to recursively correct the measurement noise. Firstly, the Sage–Husa (SH) based unscented particle filter (UPF) algorithm is proposed to estimate time-varying measurement noise adaptively in AUV path estimation for improving filtering accuracy. Secondly, the SH-based unscented Kalman filter (UKF) algorithm is proposed to enhance mapping accuracy in feature estimation. Thirdly, SAPSO-based resampling is proposed to optimize posterior particles. The random judgment mechanism is used to update feasible solutions iteratively, which makes particles disengage local extreme values and achieve optimal global effects. The effectiveness and accuracy of the proposed algorithm are evaluated through simulation and sea trial data. The average AUV navigation accuracy of the presented SAPSO-AUFastSLAM method is improved by 18.0% compared to FastSLAM, 6.5% compared to UFastSLAM, and 5.9% compared to PSO-UFastSLAM.

Vehicle dynamics prediction via adaptive robust unscented particle filter

Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

Direct tracking of noncircular sources for multiple arrays via improved unscented particle filter method

AbstractDirect tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I‐UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two‐step tracking method.

A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).

An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation

As an indispensable part of the battery management system, accurately predicting the estimation of the state of charge (SOC) has attracted more attention, which can improve the efficiency of battery use and ensure its safety performance. Taking the ternary lithium battery as the research object, we present an improved forgetting factor recursive least square (IFFRLS) method for parameter identification and a joint unscented particle filter algorithm for SOC estimation. First, take advantage of the particle swarm optimization (PSO) algorithm to select the optimal parameter initial value and forgetting factor value to improve the precision of the FFRLS method. At the same time, make use of the unscented Kalman algorithm (UKF) as the density function of the particle filter algorithm (PF) to form the unscented particle filtering (UPF) algorithm. Then, the IFFRLS method and UPF algorithm are proposed in this paper. The different working conditions results show that the proposed algorithm estimates the SOC with good convergence and high system robustness. The final estimation error of the algorithm is stable at 1.6 %, which is lower than the errors of the currently used EKF algorithm, UKF algorithm and PF algorithm, which provides a reference for future research on lithium-ion batteries.

Vehicle state and parameter estimation based on adaptive robust unscented particle filter

In order to solve the problem that the measured values of key state parameters such as the lateral velocity and yaw rate of the vehicle are easily interfered by random errors, a filter estimation method of vehicle state is proposed based on the principle of robust filtering and the unscented particle filter algorithm. Based on the establishment of a 3-DOF non-linear dynamic model and the Dugoff tire model of the vehicle, the adaptive robust unscented particle filter(ARUPF) is used to filter and estimate the parameters of the vehicle state, and to realize the longitudinal and lateral speed as well as the yaw rate of the vehicle during the driving process. The simulation and the real vehicle test results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving state estimation can be realized, the measurement parameters can be effectively filtered, and the estimation accuracy is high.

SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

Lithium-ion battery state of charge (SOC) estimation is the basis for other state estimations in electric vehicle (EV) battery management systems. Accurate SOC estimation can prevent overcharge and discharge of the battery, improve discharge efficiency and prolong cycle life. A high-precision SOC estimation method based on the gas-liquid dynamics (GLD) model is presented. The standard particle filter (PF) and the unscented particle filter (UPF) algorithm are successively used for optimizing and improving the chattering problem of the original GLD model. The results show that the two kinds of PF algorithms can effectively solve the chattering problem of the GLD model and improve the estimation accuracy. Among them, UPF has higher accuracy and the maximum estimation error is less than 2%. The PF method for estimating SOC introduced in this paper has the characteristics of high precision and large amount of calculation. Due to the above characteristics, this method can be used as a cloud SOC calculation method to periodically correct the local SOC of the vehicle.

A Novel State-of-Health Estimation for Lithium-Ion Battery via Unscented Kalman Filter and Improved Unscented Particle Filter

This paper aims to improve the rapidity and accuracy of the State of Health (SOH) assessment for lithium-ion battery. By integrating the Unscented Kalman Filter (UKF) and Improved Unscented Particle Filter (IUPF) algorithm, SOH can be effectively evaluated. The UKF algorithm is used to estimate the state of charge (SOC), the IUPF algorithm is employed to identify the ohmic internal resistance. The novelty of the proposed strategy relies on the 4-dimensional IUPF filter that is split into a 3-dimensional UKF filter and a 1-dimensional IUPF filter. Experimental results demonstrate that more accuracy and a faster rate of SOH estimation can be achieved via the UKFIUPF algorithm compared to the IUPF approach.

A Novel High-Fidelity Unscented Particle Filtering Method for the Accurate State of Charge Estimation of Lithium-Ion Batteries

Power Li-ion batteries are one of the core three powers systems of new energy vehicles, and its accurate batteries modeling and state prediction have become the core technology of the scientific and technological progress in the industry. This paper takes the ternary Li-ion batteries as the research subject. Aiming at the mathematical expressions of different structural features, innovatively construct a second-order Thevenin equivalent circuit model with autoregressive effect. This model can characterize the internal reaction mechanism of Li-ion batteries and fit the complex electrochemical reactions inside the battery. An improved particle filter model, namely a new high-fidelity unscented particle filter method, is designed and established. By introducing a suitable suggested density function, the model can accurately calculate the mean and variance, solve the particle degradation problem, and find out the Li-ion batteries state of charge, which is suitable for complex charging and discharging conditions. By further improving the theoretical analysis and combining with experiments under different working conditions, this method studies the Li-ion batteries state of charge. The test results show that the average absolute error of the improved equivalent circuit model is reduced by 0.00457 V, and the error rate is stably kept within 1%, which has the ability to describe Li-ion batteries well. When using the high-fidelity unscented particle filter algorithm to estimate the state of charge of the lithium battery, the robustness of the system is improved, the following effect is better, and the estimation error is controlled within 1.5%, which brings good practical value to the power Li-ion batteries.

A novel combined estimation method of online full‐parameter identification and adaptive unscented particle filter for Li ‐ion batteries SOC based on fractional‐order modeling

Accurate estimation of the state of charge (SOC) of Li-ion battery can ensure the reliability of the storage system. A combined estimator of online full-parameter identification and adaptive unscented particle filter for Li-ion battery SOC based on an improved fractional-order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li-ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence.

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

Vehicle State Estimation Based on Unscented Kalman Filtering and a Genetic-particle Swarm Algorithm

Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In the traditional Kalman filtering algorithm, the selection of the process noise covariance matrix and the measurement noise covariance matrix will directly affect the filtering accuracy of the algorithm. In order to improve the filtering accuracy of the filter algorithm to obtain the optimal solution, based on a 7-DOF nonlinear vehicle dynamics model and the Magic formula tire model, a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic-particle swarm algorithm (genetic-particle swarm UKF) is used to estimate several vehicle key states. Compared with traditional estimator based on UKF and the unscented particle filter (UPF), the simulation and real vehicle test results show that the proposed estimator based on the genetic-particle swarm UKF algorithm has higher accuracy and less computation requirements than the UKF estimator. And also, the proposed hybrid algorithm has superiority on the convergence speed of the optimization than the UPF algorithm. The results of a real-vehicle experiment demonstrate that the proposed hybrid algorithm can be used effectively for solving the vehicle-state estimation problem.

Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.