Particle Filter Research Articles

Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection.

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance.

An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration

Accurate prediction of remaining useful life (RUL) is crucial to the safety and reliability of the lithium-ion battery management system (BMS). However, the performance of a lithium-ion battery deteriorates nonlinearly and is heavily affected by capacity-regeneration phenomena during practical usage, which makes battery RUL prediction challenging. In this paper, a rest-time-based regeneration-phenomena-detection module is proposed and incorporated into the Coulombic efficiency-based degradation model. The model is estimated with the particle filter method to deal with the nonlinear uncertainty during the degradation and regeneration process. The discrete regeneration-detection results should be reflected by the model state instead of the model parameters during the particle filter-estimation process. To decouple the model state and model parameters during the estimation process, a dual-particle filtering estimation framework is proposed to update the model parameters and model state, respectively. A kernel smoothing method is adopted to further smooth the evolution of the model parameters, and the regeneration effects are imposed on the model states during the updating. Our proposed model and the dual-estimation framework were verified with the NASA battery datasets. The experimental results demonstrate that our proposed method is capable of modeling capacity-regeneration phenomena and provides a good RUL-prediction performance for lithium-ion batteries.

Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method.

This article is concerned with the problem of event-based remote state estimation for nonlinear/non-Gaussian systems on a wireless network with limited bandwidth. To reduce unnecessary data transmissions, a novel event-triggering mechanism is developed by using the least-square technique. Based on this, an event-triggered box particle filtering scheme is designed to realize the minimum mean-squared error estimation at the remote estimator end, in which the posterior probability density functions are calculated separately according to the information of the event-triggered indicator to avoid the problem of excessive estimation error. Different from the existing approaches, the proposed algorithm does not depend on any Gaussian assumptions and reduces the computational complexity under the premise of ensuring the estimation performance. Finally, two simulation examples are performed to demonstrate the validity of the proposed algorithm.

Vehicle Position Prediction Using Particle Filtering Based on 3D CNN-LSTM Model

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. Firstly, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Secondly, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework

Lithium-ion battery state-of-charge (SOC) estimation plays an indispensable role in the battery management system functionalities. To achieve SOC estimation with high accuracy, a precise battery model is required. Among various battery models, electrochemical model (EM) stands out for its ability to provide insights into the electrochemical mechanisms of the battery. However, the model parameters will vary with the battery running and aging, besides, the model-based methods cannot well handle the model errors. Therefore, to ensure estimation accuracy, selecting a more insightful battery model and a more effective estimation algorithm becomes crucial. This paper proposes an electrochemical model-based SOC estimation method with online parameter adjustment and an optimized algorithm. Firstly, a single particle model (SPM) with electrolyte and thermal dynamics is introduced and simplified by an approximate solution. Secondly, the particle swarm optimization (PSO) method is used to solve the problem of the particle filter (PF) algorithm, such as particle degradation and particle diversity decrease. Finally, during SOC estimating, a double-scale dual particle filter (D-PF) is utilized to adjust parameters as battery aging and temperature changes. The battery tests are applied under different working conditions and temperatures. Experimental results show that the error of SOC estimation reduces significantly compared to the previous electrochemical model-based method, and the proposed method has a higher accuracy and effectiveness.

Active regeneration particulate filter operating cycle theoretical justification

Introduction (problem statement and relevance). Particulate filters or traps used in diesel vehicles to catch suspended particles require continuous and/or periodic regeneration in order to maintain operability. The order of diesel particulate filter loading and its regeneration can be presented in the form of an algorithm. Specific features of particulate filter functioning both in regular conditions and during active regeneration have been studied in a number of scientific papers, and the results obtained in them can be considered when developing the algorithm.The purpose of the work is development of the algorithm of the operating cycle of the diesel vehicle particulate filter including modes of filter loading, active regeneration and troubleshooting.Methodology and methods. Information from scientific literature regarding specific features of diesel vehicle exhaust gas aftertreatment systems operation, namely regarding particulate filters, has been collected under this study. The study assumes a noncatalytic diesel particulate filter that is contained in the system together with a diesel oxidation catalyst, nitrogen oxide reduction catalyst and ammonia oxidation catalyst. Particulate matter sensors, temperature sensors, pressure sensors, exhaust-gas flow sensors and flow sensors for fuel to burn the soot accumulated in the filter have been used as measuring instrumentation of system indicators.Results. The created algorithm of the particulate filter operating cycle included the loading mode with definition of parameters for switching to active regeneration, definition of the transition moment between the stages of deep filtration and filtration in the soot layer. Active regeneration consisted of a sequence of two controlled fuel injections for combustion of soot in the filter and occurrence of the temperature impulse between the injections with selection of the most suitable level of exhaust gas flow rate as well as the control over the filter temperature and content of oxygen. The problems of malfunctions of the treatment system were solved by controlling the fuel supply during regeneration.Scientific novelty. The peculiarities of processes in the particulate filter during active regeneration were considered in the algorithm development, including ensuring high efficiency of soot removal from the filter.Practical significance. The algorithm can be used to develop a procedure to perform bench and driving tests of diesel vehicles as well as it can be applied in a diesel engine during vehicles operation

A UWB-Ego-Motion Particle Filter for Indoor Pose Estimation of a Ground Robot Using a Moving Horizon Hypothesis.

Ultra-wideband (UWB) has gained increasing interest for providing real-time positioning to robots in GPS-denied environments. For a robot to act on this information, it also requires its heading. This is, however, not provided by UWB. To overcome this, either multiple tags are used to create a local reference frame connected to the robot or a single tag is combined with ego-motion estimation from odometry or Inertial Measurement Unit (IMU) measurements. Both odometry and the IMU suffer from drift, and it is common to use a magnetometer to correct the drift on the heading; however, magnetometers tend to become unreliable in typical GPS-denied environments. To overcome this, a lightweight particle filter was designed to run in real time. The particle filter corrects the ego-motion heading and location drift using the UWB measurements over a moving horizon time frame. The algorithm was evaluated offline using data sets collected from a ground robot that contains line-of-sight (LOS) and non-line-of-sight conditions. An RMSE of 13 cm and 0.12 (rad) was achieved with four anchors in the LOS condition. It is also shown that it can be used to provide the robot with real-time position and heading information for the robot to act on it in LOS conditions, and it is shown to be robust in both experimental conditions.

Electrospun 3D Curly Electret Nanofiber Air Filters for Particulate Pollutants

Amidst rapid industrialization and urbanization, air pollution has emerged as a global environmental challenge. Traditional air filtration materials face challenges in effectively filtering PM0.3 and often result in discomfort due to high air resistance when used for personal protection, as well as excessive energy consumption in industrial air purification applications. This study initially utilized extremely high environmental humidity to induce fiber formation, resulting in the preparation of a fluffy fiber membrane with a three-dimensional curly morphology, which increased the porosity to 96.93%, significantly reducing air resistance during filtration. Subsequently, rutile TiO2 with a high dielectric constant was introduced, exploiting the low pressure drop characteristic of the fluffy 3D curly fiber membrane combined with the electret effect of TiO2 nanoparticles to notably improve the issue of excessive pressure drops while maintaining filtration efficiency. The microstructure, morphology, and element distribution of the fibers were analyzed using FESEM and EDS. FTIR and XRD were employed to examine the functional groups and crystal structure within the fibers. The electret effect and filtration performance of the fiber membrane were investigated using an electrostatic tester and a particulate filtration efficiency tester. The results demonstrated that inducing fiber formation under high-humidity conditions could produce fibers with a 3D curly structure. The fiber membrane was highly fluffy, significantly reducing the pressure drop. Introducing an appropriate amount of titanium dioxide markedly improved the electrostatic effect of the fiber membrane, enhancing the filtration performance of the 3D curly PVDF/TiO2 composite fiber membrane. With a 0.5% addition of TiO2 nanoparticles, the filtration efficiency of the fiber membrane reached approximately 99.197%, with a pressure drop of about 49.83 Pa. This study offers a new approach to developing efficient, low-resistance air filtration materials, showcasing the potential of material innovation in addressing air quality challenges within the sustainable development framework.

MRPSO-Loc: Multi-targets UHF RFID localization system based on MRPSO algorithm

Existing RFID localization techniques mainly focus on the location of a single target, which do not fully utilize the multi-targets identification merit of UHF RFID systems. In this paper, a multi-targets localization algorithm called merged re-shaken particle swarm optimization (MRPSO) is proposed. The ranging information and the angle information, which are obtained by using multi-frequency phase information with the help of frequency hopping technique, are merged together during the iteration process of MRPSO. In addition, based on the traditional PSO algorithm, a low weight particle filtering method was proposed, and a re-shaken scheme was introduced to improve localization accuracy. By allocating particles to each target, MRPSO can obtain each target location simultaneously after PSO process, which avoids time consuming. The experimental results show that MRPSO can achieve single-station multi-target localization with a median accuracy of 9.64 cm.

Sensor and sensor fusion technology in autonomous vehicles

The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information about the vehicles environment. Next, the paper discusses various sensor fusion algorithms, such as Kalman filters and particle filters. Furthermore, the paper explores the challenges associated with sensor fusion and addresses the issue of handling sensor failures or uncertainties. The benefits of sensor fusion technology in autonomous vehicles are also presented. These include improved perception of the environment, enhanced object recognition and tracking, better trajectory planning, and enhanced safety through redundancy and fault tolerance. Lastly, the paper discusses the advancements and highlights the integration of artificial intelligence and machine learning techniques to optimize sensor fusion algorithms and improve the overall autonomy of the vehicle. Following thorough analysis, the deduction can be made that sensor and sensor fusion technology assume a critical function in facilitating efficient and secure autonomous vehicle navigation within intricate surroundings.

Particle Filter based on Jaya optimisation for Bayesian updating of nonlinear models

Particle filter (PF) is a powerful and commonly used filtering technique based on Sequential Monte Carlo framework. The main challenge in using PF for nonlinear state and parameter estimation is the degeneracy of particles. Although resampling techniques can solve this to some extent, it would still result in particle impoverishment when a limited number of particles are used thereby affecting the accuracy. Hence, a hybrid metaheuristic optimisation algorithm that combines the PF with Jaya optimisation, (PF-JAYA) has been proposed and implemented for joint state and parameter estimation for geotechnical engineering problems. The performance of PF-JAYA has been compared against the traditional Particle Filter with Sampling Importance Resampling (PF-SIR) technique. The synthetic examples show that PF-JAYA outperforms PF-SIR in terms of accuracy, rate of convergence, parameter identification and particle diversity. Furthermore, the performance of PF-JAYA is independent of the choice of prior distribution and due to its superior convergence proves to be efficient when working with sparse monitoring information. The performance of PF-JAYA on Bayesian updating of state and parameters of an elastoplastic model for a synthetic embankment case has also been evaluated where, along with PF-SIR, the Ensemble Kalman Filter (EnKF) is also chosen for comparison. Finally a further evaluation using the Lorenz ‘63 model, shows the superior performance of PF-JAYA in terms of accuracy and precision over the classical Data Assimilation techniques.

Improved data assimilation for algal bloom dynamics simulation in the Three Gorges Reservoir using particle filter

Algal blooms have been increasingly prevalent in recent years, especially in lakes and reservoirs; their accurate prediction is essential for preserving water quality. In this study, the observed chlorophyll a (chl-a) levels were assimilated into the Environmental Fluid Dynamics Code (EFDC) of algal bloom dynamics by using a particle filter (PF), and the state variables of water quality and model parameters were simultaneously updated to achieve enhanced algal bloom predictive performance. The developed data assimilation system for algal blooms was applied to Xiangxi Bay (XXB) in the Three Gorges Reservoir (TGR). The results show that the ensemble mean accuracy and reliability of the confidence intervals of the predicted state variables, including chl-a and indirectly updated phosphate (PO4), ammonium (NH4), and nitrate (NO3) levels, were considerably improved after PF assimilation. Thus, PF assimilation is an effective tool for the dynamic correction of parameters to represent their inherent variations. Increased assimilation frequency can effectively suppress the accumulation of model errors; therefore, the use of high-frequency water quality data for assimilation is recommended to ensure more accurate and reliable algal bloom prediction.

A Geomagnetic/Odometry Integrated Localization Method for Differential Robot Using Real-Time Sequential Particle Filter.

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively.

The evaluation of catalytic activity, reaction mechanism and catalyst classification in diesel particulate filter: a review

The evaluation of catalytic activity, reaction mechanism and catalyst classification in diesel particulate filter: a review

Online state and time‐varying parameter estimation using the implicit equal‐weights particle filter

AbstractA method is proposed for resilient and efficient estimation of the states and time‐varying parameters in nonlinear high‐dimensional systems through a sequential data assimilation process. The importance of estimating time‐varying parameters lies not only in improving prediction accuracy but also in determining when model characteristics change. We propose a particle‐filter‐based method that incorporates nudging techniques inspired by optimization algorithms in machine learning by taking advantage of the flexibility of the proposal density in particle filtering. However, as the model resolution and number of observations increase, filter degeneracy tends to be the obstacle to implementing the particle filter. Therefore, this proposed method is combined with the implicit equal‐weights particle filter (IEWPF), in which all particle weights are equal. The method is validated using the 1000‐dimensional linear model with an additive parameter and the 1000‐dimensional Lorenz‐96 model, where the forcing term is parameterized. The method is shown to be capable of resilient and efficient parameter estimation for parameter changes over time in our application with a linear observation operator. This leads to the conjecture that it applies to realistic geophysical, climate, and other problems.

Factored Online Planning in Many-Agent POMDPs

In centralized multi-agent systems, often modeled as multi-agent partially observable Markov decision processes (MPOMDPs), the action and observation spaces grow exponentially with the number of agents, making the value and belief estimation of single-agent online planning ineffective. Prior work partially tackles value estimation by exploiting the inherent structure of multi-agent settings via so-called coordination graphs. Additionally, belief estimation methods have been improved by incorporating the likelihood of observations into the approximation. However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents. Therefore, we address these challenges simultaneously. First, we introduce weighted particle filtering to a sample-based online planner for MPOMDPs. Second, we present a scalable approximation of the belief. Third, we bring an approach that exploits the typical locality of agent interactions to novel online planning algorithms for MPOMDPs operating on a so-called sparse particle filter tree. Our experimental evaluation against several state-of-the-art baselines shows that our methods (1) are competitive in settings with only a few agents and (2) improve over the baselines in the presence of many agents.

Implicit unscented particle filter based indoor fusion positioning algorithms for sensor networks

Combining inertial navigation system (INS) and ultra-wideband (UWB) technologies can effectively compensate for their respective shortcomings, thus significantly enhancing the accuracy of indoor positioning systems. However, in the process of fusing these two technologies, signal fading under non-line-of-sight (NLOS) conditions, multipath effects, and errors accumulated by the INS over a long period of time are still key issues that need to be addressed. To cope with these challenges, a new fusion localization algorithm is proposed in this study. The algorithm employs a combination of fuzzy C-mean (FCM) and K-Medoids algorithms for UWB for position computation on the one hand, and an Implicit Unscented Particle Filter (IUPF)-enhanced INS for navigation information processing on the other. In addition, based on the INS error equation, this algorithm realizes the effective fusion of UWB and INS positioning information through the Minimum Error Entropy Extended Kalman Filter (MEE-EKF) technique. This integrated approach significantly improves the accuracy and stability when dealing with the localization problem in complex indoor environments. After simulation experiments under different noise conditions and real environment experiments, the algorithm proposed in this study shows significant advantages in terms of localization performance over the traditional UWB/INS localization methods in recent years. In real experiments, the algorithm achieves an average of 36.15% improvement in positioning accuracy.

Survival of the long-lived inner disk of PDS

The K7 T Tauri star remains the best laboratory for investigating the influence of giant planet formation on the structure of the parental disk. One of the most intriguing discoveries is the detection of a resolved inner disk from ALMA observations that extends up to the orbit of b. It is challenging to explain this inner disk because most of the dust particles are expected to be trapped at the outer edge of the gap opened by b and c. By performing dust evolution models in combination with radiative transfer simulations that match the gas disk masses obtained from recent thermo-chemical models of we find that when the minimum grain size in the models is larger than 0.1mu m, there is an efficient filtration of dust particles, and the inner disk is depleted during the first million year of dust evolution. To maintain an inner disk, the minimum grain size in the models therefore needs to be smaller than 0.1mu m. Only when grains are that small are they diffused and dragged along with the gas throughout the gap opened by the planets. The small grains transported in the inner disk grow and drift into it, but the constant reservoir of dust particles that are trapped at the outer edge of the gap and that continuously fragment allows the inner disk to refill on million-year timescales. Our flux predictions at millimeter wavelength of these models agree with ALMA observations. These models predict a spectral index of 3.2 in the outer and 3.6 in the inner disk. Our simple analytical calculations show that the water emission in the inner disk that was recently observed with the James Webb Space Telescope may originate from these ice-coated small grains that flow through the gap, grow, and drift toward the innermost disk regions to reach the water snowline. These models may mirror the history and evolution of our Solar System, in which Jupiter and Saturn played a crucial role in shaping the architecture and properties of the planets.

PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations.

Advanced Electrospinning Technology Applied to Polymer-Based Sensors in Energy and Environmental Applications.

Due to its designable nanostructure and simple and inexpensive preparation process, electrospun nanofibers have important applications in energy collection, wearable sports health detection, environmental pollutant detection, pollutant filtration and degradation, and other fields. In recent years, a series of polymer-based fiber materials have been prepared using this method, and detailed research and discussion have been conducted on the material structure and performance factors. This article summarizes the effects of preparation parameters, environmental factors, a combination of other methods, and surface modification of electrospinning on the properties of composite nanofibers. Meanwhile, the effects of different collection devices and electrospinning preparation parameters on material properties were compared. Subsequently, it summarized the material structure design and specific applications in wearable device power supply, energy collection, environmental pollutant sensing, air quality detection, air pollution particle filtration, and environmental pollutant degradation. We aim to review the latest developments in electrospinning applications to inspire new energy collection, detection, and pollutant treatment equipment, and achieve the commercial promotion of polymer fibers in the fields of energy and environment. Finally, we have identified some unresolved issues in the detection and treatment of environmental issues with electrospun polymer fibers and proposed some suggestions and new ideas for these issues.