Particle Impoverishment Problem Research Articles

Bearing Fault Prognosis Method Based on Priori Knowledge-Enhanced Particle Filter

A priori knowledge-enhanced particle filter (PKE-PF) method is proposed to solve the problem of particle impoverishment in bearing fault prognosis with incomplete data. Based on the existing bearing life degradation data and the parameter transfer method in the transfer learning theory, particle initialization optimization, which is very important in the PF method, is carried out to effectively improve particle effectiveness and avoid the problem of premature particle exhaustion. Based on the whole-life degradation experiment of rolling bearings, the validation results show that the traditional PF method and its improved method are prone to particle exhaustion, which seriously affects the fault prediction results. The PKE-PF method proposed in this paper can effectively avoid the problem of premature particle depletion and obtain a more ideal fault prognosis results.

A health state prediction method of traction converter IGBT based on optimized particle filter

Insulated gate bipolar transistor (IGBT), as the most critical and vulnerable power electronic component in the field of rail transportation, its reliability has become a research hotspot in recent years. To improve the safe and reliable operation of rail transportation and to reduce the maintenance cost by rationalizing system maintenance, it is critical to monitor the health of the traction converter IGBTs. This paper first reviews the failure mechanism of the IGBT, then analyzes the health state characterization parameters of the IGBT by the accelerated aging test, selects thermal resistance as the health state characterization parameter of the IGBT, and establishes the degradation model of the IGBT. On this basis, a health state prediction method of IGBT based on the genetic algorithm unscented particle filter (GA-UPF) is proposed. This method uses an unscented Kalman filter to generate important proposal density distribution for each particle, estimates, and updates the parameters of the degradation model, alleviates the particle degradation problem, and uses a genetic algorithm to solve the particle impoverishment problem introduced by the traditional resampling method, which improves the particle diversity and prediction accuracy. Mahalanobis distance is used to monitor the abnormal state of the thermal resistance. After the abnormal occurs, the value of the thermal resistance is predicted according to the degradation model and updated parameters. The degradation trend of the thermal resistance is predicted to reflect the health state of the IGBT. Experiments on IGBT degradation data show that the proposed method can provide higher health state prediction accuracy than the traditional particle filter method.

An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter

The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly.

Error Constraint Enhanced Particle Filter Using Quantum Particle Swarm Optimization

The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles’ diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods.

A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications.

Visual object tracking via iterative ant particle filtering

Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non-linear/non-Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)-based iterative PF for object tracking. In the proposed method, the basic idea of ACO is used to simulate the behaviour of a particle moving toward the posterior distribution. Such idea is incorporated into the particle filtering framework in order to overcome the well-known particle impoverishment problem. An iterative unscented Kalman filter is used to design a proposal distribution for particle generation in order to generate better predicted sample states. For the likelihood model, the authors adopt the locality sensitive histogram to model the appearance of the target object, which can better handle the illumination variation during tracking. The experimental results demonstrate that the proposed tracker shows better performance than the other tracking methods.

Navigation of Unmanned Surface Vehicles Using Underwater Geophysical Sensing

Underwater geophysical properties can provide useful information for surface navigation, particularly in situations where a global navigation satellite system is unavailable. Unmanned surface vehicles (USVs) equipped with geophysical sensors can measure certain types of underwater properties related to Earth geophysics. For example, multibeam echosounders can obtain an array of sonar ranges for underwater terrains, and magnetometers can measure geomagnetic vector fields. These measurements can be used to track vehicle poses if pre-surveyed geophysical maps are provided. This paper proposes geophysical navigation of USVs using a multibeam sonar and magnetometer. The navigation algorithm is implemented within a particle filter framework, and we designed observation models for each geophysical sensor. To avoid the particle impoverishment problem of the conventional terrain based navigation, a terrain roughness measure is employed to modify the weight update and resampling steps of the standard particle filter framework. We conducted field experiments in an inland water environment using the designed surface vehicle, and validated enhanced tracking performance of the proposed methods by comparing the methods with conventional approaches.

Multi-target tracking method based on improved firefly algorithm optimized particle filter

Joint probabilistic data association-particle filter (JPDA-PF) algorithm is often used to solve the problem of data association in multi-target tracking, it has the advantages of particle filter in dealing with nonlinear and non-Gaussian systems, but at the same time, the resampling process of particle filter will lead to the problem of particle impoverishment, which will affect the tracking accuracy. In this article, a new multi-target tracking method based on particle filter optimized by improved firefly algorithm is proposed. To solve the problem of weight degradation and avoid the problem of particle impoverishment, the improved firefly algorithm replaces the traditional resampling method and optimizes the distribution of particles in particle filter. According to the simulation results, the improved algorithm has better tracking accuracy than JPDA-PF as the number of particles is equal.

Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Bearing is the major contributor to wind turbine gearbox failures. Accurate remaining useful life prediction for drivetrain gearboxes of wind turbines is of great importance to achieve condition-based maintenance to improve the wind turbine reliability and reduce the cost of wind power. However, remaining useful life prediction is a challenging work due to the limited monitoring data and the lack of an accurate physical fault degradation model. The particle filtering method has been used for the remaining useful life prediction of wind turbine drivetrain gearboxes, but suffers from the particle impoverishment problem due to a low particle diversity, which may lead to unsatisfactory prediction results. To solve this problem, this paper proposes an enhanced particle filtering algorithm in which an adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resampling process to solve the particle impoverishment problem. The enhanced particle filtering algorithm is applied successfully to predict the remaining useful life of a bearing in the drivetrain gearbox of a 2.5 MW wind turbine equipped with a doubly-fed induction generator.

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and non-Gaussian LSMs. However, the standard PF always suffers from the particle impoverishment problem, characterized by loss of particle diversity. To solve this problem, an immune evolution particle filter with MCMC simulation inspired by the biological immune system, entitled IEPFM, is proposed for DA in this paper. The merit of this approach is in imitating the antibody diversity preservation mechanism to further improve particle diversity, thus increasing the accuracy of estimates. Furthermore, the immune memory function refers to promise particle evolution process towards optimal estimates. Effectiveness of the proposed approach is demonstrated by the numerical simulation experiment using a highly nonlinear atmospheric model. Finally, IEPFM is applied to a soil moisture (SM) assimilation experiment, which assimilates in situ observations into the Variable Infiltration Capacity (VIC) model to estimate SM in the MaQu network region of the Tibetan Plateau. Both synthetic and real case experiments demonstrate that IEPFM mitigates particle impoverishment and provides more accurate assimilation results compared with other popular DA algorithms.

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Infrared small target detection and tracking algorithm based on new closed-loop control particle filter

The problem of particle impoverishment could be always found in standard particle filter, additionally a large number of particles are required for accurate estimation. as it is difficult to meet the demand of modern infrared search and tracking system. To solve this problem, an improved infrared small target detection and tracking method based on closed-loop control bat algorithm optimized particle filter is proposed. Firstly, bat algorithm is introduced into the particle filtering in this method. Particles are used to simulate the process that an individual bat hunts and avoids obstacles so that particles move towards the high-likelihood region. Meanwhile, the improved algorithm takes the proportion of particles accepting a new state as the feedback quantity and proposes to conduct dynamic control on global and local search ability of particle filtering by closed-loop control strategy, which further improves the overall quality of particle distribution. The performance of the improved detection and tracking algorithm is tested in simulation scene and real scene of infrared small target. Experimental results show that the improved algorithm improves the performance of the infrared searching and tracking system.

Application of deterministic resampling particle filter to fatigue prognosis

The method based on a particle filter for a fatigue crack growth prognosis has proved to be a powerful and effective tool for developing prognostics and health management (PHM) technology. However, the widely used basic particle filter have the unavoidable particle impoverishment problem, which will make particles unable to approximate the true posterior probability density function of the system state and lead to a prognosis result with a large error. This paper proposes a fatigue crack growth prognosis method based on a deterministic resampling particle filter. The active structural health monitoring based on the Lamb wave is used for on-line crack length monitoring with piezoelectric transducers. With the on-line crack measurement, the crack state and crack growth model parameters are estimated for a fatigue crack growth prognosis. In addition, the deterministic resampling procedure is employed to overcome the particle impoverishment problem. The result shows the proposed crack growth prognosis method based on deterministic resampling particle filter can provide more satisfactory results than the basic particle filter.

A novel SMC-PHD filter based on particle compensation

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, the particle impoverishment problem introduced by the resampling step, together with the high computational burden problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this work, a novel SMC-PHD filter based on particle compensation is proposed to solve above problems. Firstly, according to a comprehensive analysis on the particle impoverishment problem, a new particle generating mechanism is developed to compensate the particles. Then, all the particles are integrated into the SMC-PHD filter framework. Simulation results demonstrate that, in comparison with the SMC-PHD filter, proposed PC-SMC-PHD filter is capable of overcoming the particle impoverishment problem, as well as improving the processing rate for a certain tracking accuracy in different scenarios.

Intelligent particle filter based on bat algorithm

Particle filer is apt to have particle impoverishment with unstable filtering precision, and a large number of granules are required to estimate the nonlinear system accurately, which reduces the comprehensive performance of the algorithm. To solve this problem, a new particle filter based on bat algorithm is presented in this paper, where particles are used to represent individual bat so as to imitate the search process of bats for preys. In traditional resampling process, particles are directly discarded, the improved algorithm adopts another approach and solves the problem of particle impoverishment. It combines the advantages of particle swarm optimization algorithm and harmonic algorithm perfectly. New particle filter has capacity of global and local search and is superior in computation accuracy and efficiency. By adjusting frequency, loudness, and impulse emissivity of particle swarm, the optimal particle at that time is followed by particle swarm to search in the solution space. The global search and local search can be switched dynamically to improve the overall quality of the particles swarm as well as the distribution rationality. In addition, the improved particle filter uses Lvy flight strategy to avoid being attracted by harmful local optimal solution, it expands the space of research and further promotes the optimization effect of particle distribution. Using the useful information about particle swarm, improved particle filter can make particles get rid of local optimum and reduce the waste of iterations in insignificant status change. Based on the number of valid particle samples, it can improve quality of particle samples by expanding their diversity. In information interaction mechanism of improved particle filter, the method in this paper sets scoreboard of particle target function to compare the value of particle target function at each iteration sub-moment with the value of target function on scoreboard to gain global optimum of all particles at current filtering moment. Taking information interaction between global optimum and particle swarm, the guiding function of global optimum is realized. The process of particle optimization is ended prematurely through setting a maximum iteration or termination threshold. There is a tendency for the whole particle swarm closing to high likehood area without global convergence so that the advantages of improved particle filter in accuracy and speed will not be damaged. In addition, convergence analysis and computational complexity analysis are given in this paper. Experiment indicates that this method can improve the particle diversity and prediction accuracy of particle filter, and meanwhile reduce the particle quantity obviously which is required by the state value prediction for nonlinear system.

A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem

As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

Joint Estimation of CFO and Channel in OFDM Systems with Blind Noise Statistics

ABSTRACTWhile inter-symbol interference is not of concern, inter-carrier interference that is caused by carrier frequency offset (CFO) is more concerned in orthogonal frequency division multiplexing (OFDM) systems. The estimation of channel impulse response (CIR) is another important parameter of interest in the system. Recently, a number of approaches for the joint estimation of CFO and CIR in OFDM systems have been proposed in the literature, and most of them are traditional maximum-likelihood-based approaches. In this paper, we propose a number of mixture cost-reference particle filtering (CRPF) approaches for joint estimation of CIR and CFO, and a couple of them can be applied without the knowledge of both state and measurement noise statistics. Conventional least mean squares algorithm or recursive least squares algorithm is jointly employed with recently developed CRPF for nonlinear and multiple parameters estimation in OFDM systems. The computer simulation results show that mixture CRPFs are more robust against particle impoverishment (PI) problem than standard mixture particle filtering (PF) and mixture regularized PF (RPF) approaches although RPF was particularly developed to tackle the PI problem. In this paper, we investigate a number of mixture PF approaches including ones that do not need the information of noise statistics for the joint estimation of CFO and channel in OFDM systems.

State Estimation in Nonlinear System Using Sequential Evolutionary Filter

As a commonly encountered problem in the particle filters (PFs), the particle impoverishment is caused partially by the reduction of particle diversity after resampling. In this paper, a novel particle filtering technique named sequential evolutionary filter (SEF) is introduced, by which the particle impoverishment problem can be effectively mitigated. SEF is proposed based on the genetic algorithm (GA). A GA-inspired strategy is designed and incorporated in SEF. With this strategy, the resampling used in most of the existing PFs is not necessary, and the particle diversity can be maintained. The experimental results also demonstrate the effectiveness of SEF.

Intelligent Particle Filter and Its Application on Fault Detection of Nonlinear System

The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.

A particle filtering and kernel smoothing-based approach for new design component prognostics

This work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a kernel smoothing (KS) method. This PF–KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF–KS method can provide more satisfactory results than the traditional PF methods.