Improved Particle Filter Algorithm Research Articles

A Novel Coupled State/Load/Parameter Identification Method Based on the Improved Particle Filter Algorithm

There have been increasing research studies focusing on the identification of structural load with uncertain model in recent years. This paper presents a novel approach based on the improved Particle Filter (PF) algorithm for the nonlinear structural system. The main strategy of the approach is the fusion of the weighted least-square algorithm and the conventional PF algorithm. The weighted least-square algorithm is derived for the estimation of the unknown loads, and the PF algorithm is used for the identification of the augmented states which includes the structural displacement, velocity and unknown parameters. Additionally, the parallel algorithm is adopted for the improvement of computing speed, and a resampling step is also used for alleviating the degeneracy of the particles to improve the accuracy. A numerical example modelling as a four-story hysteretic shear-beam building is studied to validate the capability of the presented approach.

Retracted: Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Retracted: Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Research on Object Trajectory Detection System Based on Computer Vision

The main task of object trajectory detection is to collect and synthesize all kinds of information generated in the process of object motion, and get the displacement information of object in 3D space. Vision is one of the important ways for human beings to perceive the external environment and the cognitive world, and it plays a very important role in human life. With the development of computer technology and signal processing technology, a computer vision discipline based on the principle of simulating human eyes has gradually formed. In this paper, an object trajectory detection system based on computer vision is studied to realize object trajectory detection. In this paper, an improved particle filter algorithm is used to track moving objects. The research results show that the improved particle filter algorithm effectively reduces the influence of calculation error and nonlinear error on the measurement system and improves its accuracy. The practical application results show that the measurement error of the system can be controlled at about 5% when considering various error factors, which can meet the needs of practical application.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning (PDR) and geomagnetic technology have the problems of large initial position error, low sensor accuracy, and geomagnetic mismatch. In this study, a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed, where Wi-Fi, PDR, and geomagnetic signals are integrated to improve indoor positioning performances. One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm. During the positioning process, an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively. The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected, which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm. In addition, this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving, which satisfies the real-time requirement of our fusion positioning approach. Through experimental verification, the average positioning accuracy of the proposed approach reaches 1.59 m, which improves 33.2% compared with the existing particle filter fusion positioning algorithms.

State-of-health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm

State-of-health (SOH) and remaining useful life (RUL) are vital indicators closely related to the safety of lithium-ion batteries (LIBs). In this study, an online capacity estimation and offline RUL prediction methods based on an improved particle filter and recursive-least-square (PF-RLS) algorithm are proposed. In this method, the characteristic voltage (CV) is extracted from the discharge curve as a health feature, and the correlation model of CV-cycles-capacity is established. Then, an improved PF-RLS algorithm is used to estimate the CV in real-time to realize SOH estimation and RUL prediction. In the improved PF-RL algorithm, the initial value of the proposed probability density is optimized by fitting the sample battery aging data to improve the accuracy and rapidity of the model parameter identification. The results show that the prediction accuracy and stability of the improved PF-RLS algorithm are better than those of the standard PF algorithm. The SOH estimation error can be kept within 3 %, and the RUL prediction error can be kept within 5 % during the battery aging process.

Remaining Useful Life Prediction for Rolling Bearings With a Novel Entropy-Based Health Indicator and Improved Particle Filter Algorithm

Due to the importance of bearings in modern machinery, the prediction of the remaining useful life (RUL) of rolling bearings has been widely studied. When predicting the RUL of rolling bearings in engineering practice, the RUL is usually predicted based on historical data, and as the historical data increases, the prediction results should be more accurate. However, the existing methods usually have the shortcomings of low prediction accuracy, large cumulative error and failure to dynamically give prediction results with the increase of historical data, which are not suitable for engineering practice.To address the above problems, a novel RUL prediction method is proposed. The proposed method consists of 3 parts: First, the multi-scale entropy-based feature – namely “average multi-scale morphological gradient power spectral information entropy (AMMGPSIE)” – from the rolling bearings as the Health indicator (HI) is extracted to ensure all the fault-related information is well-included; Then, the HI is processed with the enhanced Hodrick Prescott trend-filtering with boundary lines (HPTF-BL) to ensure good performance and small fluctuation on the HI; Finally, the deterioration curve is predicted using an LSTM neural network and the improved Particle Filter algorithm that we proposed. The proposed method is validated using the experimental bearing degradation dataset and the casing data of a centrifugal pump bearing from an actual industrial site. Comparing the results with other recent RUL prediction methods, the proposed method achieved state-of-the-art feasibility and effectiveness, conform to the needs of practical application of the project.

The Rescuer’s Navigation in Metro Stations Based on Inertial Sensors and WiFi

The demand for metro station rescue navigation is increasing. This paper presents an improved particle filter to challenge the navigation problem in metro stations. A particle filter is often used to estimate the position of pedestrians. However, the particle-impoverishment problem is inevitable. To solve this problem, a dingo optimization algorithm (DOA) with global search ability is introduced, and an improved particle filter called a dingo particle filter (DPF) is proposed. Dead reckoning (DR) is taken as the system equation, and WiFi matching results are used as the observation equation. The improved particle filter algorithm introduces a dingo optimization algorithm to improve the diversity of particles and effectively reduce the particle-impoverishment problem. The experimental results show that the average positioning accuracy is 1.1 m and 1.2 m.

Life Prediction Method of Electromagnetic Contactor Based on Fusion of Improved Particle Filter and Degradation Model

Electromagnetic contactors are the key components of high-speed trains. A reasonable evaluation of the remaining life of the contactors can optimize the replacement cycle of the contactors and ensure the safe operation of the train. Aiming at the problem of remaining life prediction of contactors, a contactor life prediction method based on the fusion of improved particle filter algorithm and performance degradation model is proposed. Firstly, on the basis of analyzing the failure mechanism of the contactor and establishing its electromagnetic and dynamic simulation model, a performance degradation model based on overtravel time is established. Then design the contactor life test to obtain its full life cycle degradation data. Next, the adaptive genetic algorithm is used to optimize the resampling process of the particle filter algorithm. Finally, the optimal parameter values of the performance degradation model are obtained by the improved particle filter algorithm, and then the prediction of the remaining life of the contactor is realized. The prediction results show that the method can effectively predict the remaining life of the contactor, and the prediction accuracy meets the actual engineering needs, which provides an important reference for the operation and maintenance of the electromagnetic contactor for high-speed trains.

Prediction of fatigue crack propagation in metals based on IBAS-PF

Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in battery failure prediction and health management (PHM). By accurately predicting the RUL of the battery, the battery can be replaced accordingly, thereby effectively avoiding the occurrence of an accident and ensuring the normal operation of the entire system. In the prediction of the remaining service life of lithium-ion batteries, it is difficult to ensure accuracy due to the problem of particle degradation and the influence of singular values in the particle filter algorithm. In view of these problems, this article introduces the unscented Kalman algorithm to improve the particle filter algorithm from the perspective of re-weighting the particles, so as to improve the accuracy of the prediction results of the remaining service life of lithium-ion batteries. The improved particle filter is simulated and verified using the battery sample data in the Arbin experimental test platform. Comparing the simulation results with the traditional particle filter method, when the number of reference samples is the same, the PDF width of the prediction results of the improved particle filter algorithm is slightly smaller than that of the particle filter algorithm, indicating that the fluctuation of the prediction result is more accurate. It is proved that the improved particle filter method proposed in this article can provide more accurate battery RUL prediction results and can effectively improve the accuracy and robustness of the remaining service life prediction of lithium-ion batteries.

Firefly optimized particle filter algorithm based on adaptive differential evolution

The improved particle filter algorithm based on fireflies reduces the particle diversity in the later iterations, which is easy to fall into the problem of local optimization. To solve the problem, after firefly position updated, we can integrate the differential evolution algorithm into it. The process can be described like this: firstly, the mutation process is guided by the particle weight adaptively; the crossover process is selecting individuals randomly according to the crossover probability; finally, the selection process takes the observation probability density function as the fitness value, and retains individuals with high fitness. After adding differential evolution, the diversity of individuals has increased and jumped out the process of local optimization. The overall quality of the particle swarm has been improved. Experiments show that the tracking accuracy of the improved algorithm has been improved, as well as the global optimization capabilities.

Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms

Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.

A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.

SOC and SOH Joint Estimation of Lithium-Ion Battery Based on Improved Particle Filter Algorithm

SOC and SOH Joint Estimation of Lithium-Ion Battery Based on Improved Particle Filter Algorithm

Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg-Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real-time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real-time localizing of the restaurant service robot.

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.

Tracking System and Its Application in Unmanned Automobile Navigation Based on Sparse Photoelectric Sensor Network

Sensor tracking technology has broad prospects of application in the fields of smart home and environmental protection. The passive motion tracking method of sensor networks can realize the perception of location, temperature and other information without carrying sensor nodes. A sparse network tracking system based on infrared sensor nodes is proposed in this study, which can control the running automobiles with unmanned navigation. On the basis of the theory of diffraction, the way of spreading for wireless received signal strength (RSS) can be divided into "scattered waves" and "diffracted waves," which can be regarded as two components of infrared sensing wireless signals so as to further propose the RSS indicators of "long-term testing value" and "short-term test value." Based on these indicators, a measurement model based on diffraction effects and scattering effects is proposed, and an improved particle filter algorithm is used to update the motion tracking. The hardware design of each module in an unmanned vehicle includes the main controller, tracking circuit, serial port circuit, motor control circuit and infrared sensor control circuit of the car. In the experiment, the measurement accuracy of the tracking system based on the sparse infrared photoelectric sensor was first tested. In the simulation experiment, the long-term test value, the short-term test value and the actual measurement value were compared respectively. The test results show that the theoretical RSS value and the actual test result can be matched. Moreover, the infrared photoelectric tracking system is used to design the navigation control system of unmanned cars, helping the car to drive automatically through obstacle avoidance test and tracking obstacle avoidance test.

Wireless sensor indoor positioning based on an improved particle filter algorithm

Positioning by wireless sensor network is one of its main functions and has been widely used in many fields. However, when signal propagation is hindered, serious errors, non-line-of-sight errors, occur. In order to solve this problem, this article proposes an improved particle filter algorithm, which introduces the idea of residual analysis to improve reliability. The algorithm assigns weights to the particles based on the residuals and selects the appropriate particles. In addition, the non-line-of-sight error parameter [Formula: see text] is introduced, and the second selection is made according to [Formula: see text], which considers the influence of non-line-of-sight error. The non-line-of-sight error is greatly reduced after two selections. The simulation is performed under several different non-line-of-sight errors, and results show that the algorithm is superior to Kalman filter and particle filter.

Accurate Position Estimation of Mobile Robot Based on Cyber-Physical-Social Systems (CPSS)

When planning the trajectory of a mobile robot, it is usually necessary to use sensors to collect a large amount of position information. Because traditional computing methods cannot effectively use huge data sources, an edge computing that implements edge intelligent services on the side of network edge near the data source is proposed, which speeds up the process of data processing. However, the data collected through sensors may contain gross errors. In general, the influence of gross errors on state estimation are rarely considered when using particle filter algorithms for state estimation. In fact, the measurements of dynamic systems are often affected by different types of gross errors in the actual application process. Therefore, it is a problem worth studying that how to detect and compensate for different types of gross errors. In this paper, an improved particle filter algorithm is proposed for the position estimation of mobile robot dynamic system. Firstly, the gross error identification method is used to identify the types of gross errors, and then the various gross errors are compensated. Finally, the particle filter algorithm based on the measurements compensation is obtained. Simulation experiments on the position estimation of mobile robots are carried out to verify the effectiveness of the proposed method in solving the measurements with gross errors. The precise position estimation of the mobile robot is achieved. Through the simulation experiments on the position estimation problem of mobile robots, it is verified that the proposed method is effective in solving the measurements with gross errors. And the accurate position estimation of the mobile robot is realized.

Remaining Useful Life Prediction Based on an Adaptive Inverse Gaussian Degradation Process With Measurement Errors

Remaining useful life (RUL) prediction plays a crucial role in prognostics and health management (PHM). Recently, the adaptive model-based RUL prediction, which is proven effective and flexible, has gained considerable attention. Most research on adaptive degradation models focuses on the Wiener process. However, since the degradation process of some products is accumulated and irreversible, the inverse Gaussian (IG) process that can describe monotonic degradation paths is a natural choice for degradation modelling. This article proposes a nonlinear adaptive IG process along with the corresponding state space model considering measurement errors. Then, an improved particle filtering algorithm is presented to update the degradation parameter and estimate the underlying degradation state under the nonGaussian assumptions in the state space model. The RUL prediction depending on historical degradation data is derived based on the results of particle methods, which can avoid high-dimensional integration. In addition, the expectation-maximization (EM) algorithm combined with an improved particle smoother is developed to estimate and adaptively update the unknown model parameters once newly monitored degradation data become available. Finally, this article concludes with a simulation study and a case application to demonstrate the applicability and superiority of the proposed method.