Performance Of Particle Filter Research Articles

Particle filter performance of soot-loaded diesel particulate filter and the effect of its regeneration on the particle number and size distribution

The DPF regeneration is crucial for reducing backpressure, minimizing negative impacts on engine power and fuel economy, but the changes in particulate emission characteristics it brings are worth studying. In this study, the influence of DPFs with different soot loadings on exhaust back pressure and particle emission characteristics, as well as the particle number emission, particle size distribution, and thermal field distribution characteristics during regeneration was investigated based on engine bench test. Results reveal that the exhaust back pressure increases linearly with the engine speed, as well as the soot loading. The increase of DPF soot loading leads to a considerable increase in the reduction efficiency of particle number (PN) and particulate matter emissions, especially for the accumulation mode particles. Moreover, the performance in reducing nucleation mode particles is markedly better than that of accumulation mode ones, and this effect is greatly influenced by the engine operating conditions. At the beginning of DPF regeneration, the PN concentration increases sharply, especially the nucleation mode particles. After regeneration, the PN emissions decrease by two orders of magnitude. During regeneration, the particle size distribution exhibits a unimodal distribution, with the peak value increasing considerably and shifting toward smaller particle sizes. After regeneration, the particle size of the PN peak value slightly decreases and markedly shifts toward larger sizes, the accumulation mode particle concentration increases by 7.2 times, and the nucleation mode particle concentration increases by 2.6 times. Before the DPF regeneration, the axial temperature gradually decreases along the direction of the airflow, and the radial temperature gradually decreases from the center to the edge. During regeneration, the highest temperature occurs in the center of the DPF cross-section, whereas the temperature peak at the outlet interface occurs at the middle position of the DPF. The findings of this paper can provide scientific references to formulation and optimization of DPF regeneration strategies.

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.

On the performance of particle filters with adaptive number of particles

We investigate the performance of a class of particle filters (PFs) that can automatically tune their computational complexity by evaluating online certain predictive statistics which are invariant for a broad class of state-space models. To be specific, we propose a family of block-adaptive PFs based on the methodology of Elvira et al. (IEEE Trans Signal Process 65(7):1781–1794, 2017). In this class of algorithms, the number of Monte Carlo samples (known as particles) is adjusted periodically, and we prove that the theoretical error bounds of the PF actually adapt to the updates in the number of particles. The evaluation of the predictive statistics that lies at the core of the methodology is done by generating fictitious observations, i.e., particles in the observation space. We study, both analytically and numerically, the impact of the number K of these particles on the performance of the algorithm. In particular, we prove that if the predictive statistics with K fictitious observations converged exactly, then the particle approximation of the filtering distribution would match the first K elements in a series of moments of the true filter. This result can be understood as a converse to some convergence theorems for PFs. From this analysis, we deduce an alternative predictive statistic that can be computed (for some models) without sampling any fictitious observations at all. Finally, we conduct an extensive simulation study that illustrates the theoretical results and provides further insights into the complexity, performance and behavior of the new class of algorithms.

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems usually benefit from the high density of visual features. When using filter-based solutions to localize the robot, such an environment usually uses a high number of particles to perform accurately. These two facts can lead to computationally expensive localization algorithms that are intended to perform in real-time. This work proposes a refinement step to a standard high-dimensional filter-based localization solution through the novelty of downsampling the filter using an online clustering algorithm and applying a scan-match procedure to each cluster. Thus, this approach allows scan-matchers without high computational cost, even in high dimensional filters. Experiments using real data in an agricultural environment show that this approach improves the Particle Filter performance estimating the robot pose. Additionally, results show that this approach can build a precise 3D reconstruction of agricultural environments using visual scans, i.e., 3D scans with RGB information.

Investigative Analysis of Suboptimal Filter for State Estimation in Object Tracking Wireless Sensor Network

The research presented here is the investigation of particle filter (PF) performance in a state estimation of a point mass type noncooperative moving target in an object tracking wireless sensor network (OTWSN). Motion dynamics of the target is modeled as a stochastic state-space model by simulating different time-varying parameters of a jump Markov linear system in a finite way considering these parameters are sufficiently large to meet the practical needs. Performance is evaluated on the basis of the lower bound of the posterior Cramer–Rao bound as root-mean-square error (RMSE). It has been found that the performance is subject to dynamics of the target motion and sensor observation process under consideration. The present research focuses on multiple motion dynamics, keeping in mind the real-time noncooperative moving target tracking in OTWSN. Results indicate a positive correlation between RMSE and motion models. Correlation increases as the motion dynamics change, but still, PF demonstrates suboptimal response to motion dynamics.

Multiple dipole source localization of EEG measurements using particle filter with partial stratified resampling.

Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracylimits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.

Likelihood Induced by Moment Functions using Particle Filter: A Comparison of Particle GMM and Standard MCMC Methods

Particle filtering is a useful statistical tool which can be used to make inference on the latent variables and the structural parameters of state space models by employing it inside MCMC algorithms (Flury and Shephard, 2011). It only relies on two assumptions (Gordon et al, 1993): a: The ability to simulate from the dynamic of the model; b: The predictive measurement density can be computed. In practice the second assumption may not be obvious and implementations of particle filter can become difficult to conduct. Gallant, Giacomini and Ragusa (2016) have recently developed a particle filter which does not rely on the structural form of the measurement equation. This method uses a set of moment conditions to induce the likelihood function of a structural model under a GMM criteria. The semiparametric structure allows to use particle filtering where the standard techniques are not applicable or difficult to implement. On the other hand, the GMM representation is less efficient than the standard technique and in some cases it can affect the proper functioning of particle lter and in turn deliver poor estimates. The contribution of this paper is to provide a comparison between the standard techniques, as Kalman filter and standard bootstrap particle filter, and the method proposed by Gallant et al (2016) in order to measure the performance of particle filter with GMM representation.

Effect of leakage and blockage on the acoustic behavior of particle filters

The effects of leakage and blockage on the acoustic performance of particle filters have been examined by using one-dimensional acoustic analysis and experimental methods. First, the transfer matrix of a filter system connected to inlet and outlet pipes with conical sections is measured using a two-load method. Then, the transfer matrix of a particle filter only is extracted from the experiments by applying inverse matrices of the conical sections. In the analytical approaches, the one-dimensional acoustic model for the leakage between the filter and the housing is developed. The predicted transmission loss shows a good agreement with the experimental results. Compared to the baseline, the leakage between the filter and housing increases transmission loss at a certain frequency and its harmonics. In addition, the transmission loss for the system with a partially blocked filter is measured. The blockage of the filter also increases the transmission loss at higher frequencies. For the simplicity of experiments to identify the leakage and blockage, the reflection coefficients at the inlet of the filter system have been measured using two different downstream conditions: open pipe and highly absorptive terminations. The experiments show that with highly absorptive terminations, it is easier to see the difference between the baseline and the defects.

Improvement of Maximum Variance Weight Partitioning Particle Filter in Urban Computing and Intelligence

At present, urban computing and intelligence has become an important topic in the research field of artificial intelligence. On the other hand, computer vision as a crucial bridge between urban world and artificial intelligence is playing a key role in urban computing and intelligence. Conventional particle filter is derived from Karman filter, which theoretically based on Monte Carlo method. Sequential importance resampling (SIR) is implemented in conventional particle filter to avoid the degeneracy problem. In order to overcome the shortcomings of the resampling algorithm in the traditional particle filter, we proposed an optimized particle filter using the maximum variance weight segmentation resampling algorithm in this paper, which improved the performance of particle filter. Compared with the traditional particle filter algorithm, the experimental results show that the proposed scheme outperforms in terms of computational consumption and the accuracy of particle tracking. The final experimental results proved that the quality of the maximum variance weight segmentation method increased the accuracy and stability in motion trajectory tracking tasks.

Simulation Study on Influence of Fuel Injection Strategy on Regeneration Performance of Catalytic Particle Filter

The influence rule of fuel injection strategy on regeneration performance of catalytic particle filter (CPF) will be studied in this paper, through using system dynamics, GT-POWER and AVL FIRE to build and match respectively the onedimensional complete machine model of D19 high pressure common rail diesel engine of variable geometry turbocharger (VGT) and the three-dimensional simulation model of CPF, which are based upon the 50% load working condition with 3000r/min. The study shows that with the delay of main injection timing, the regeneration rate of CPF gradually increase, simultaneously, residual volume of particulate matter (PM), pressure drop and NO2 of CPF at exit end decrease. At the earlier main injection timing, with increasing exhaust gas recirculation (EGR) rate, regeneration rate of CPF is decreasing and residual volume of PM is increasing dramatically. At the later main injection timing, with increasing EGR rate, PM residual volume shows trend of rise then descend. Adoption of post injection benefits for regeneration of CPF, with delay of post injection timing, pressure drop and PM residual volume of CPF both show trend of rise then descend, the post injection timing corresponding with the best efficiency of CPF regeneration will be forward lead with increasing of EGR rate. Appropriately enhancing amount of post injection will benefit for regeneration of CPF. And, the negative effect of CPF regeneration will be increased when using post injection and later timing to rise EGR rate.

Robust Likelihood Model for Illumination Invariance in Particle Filtering

Tracking visual targets in an unconstrained environment is challenging due to variations in illumination, scale, occlusion, and motion blur, for example. Many video applications that utilize particle filter-based visual target trackers require tracking of visual targets under varying illuminations. Similarity measures and likelihood estimation strongly influence the performance of particle filters. In this paper, we propose a novel likelihood estimator that has been combined with other state-of-the-art particle filtering-based tracking techniques to accommodate varying illumination by predicting changes in the illumination intensity and direction of the illumination. Moreover, an enhanced update strategy for the template dictionary is used along with a sparse representation model to solve the problem of drift due to appearance changes during tracking. The proposed algorithm has been evaluated using various particle-filter-based tracking algorithms on scenes from public data sets and using our gesture data set, which includes variations in illumination. Using the proposed model, the algorithms perform up to 20% better on sequences for which variations in illumination are dominant. We carried systematic experiments to evaluate the robustness of the proposed algorithm on video sequences with illumination variations, as well as other variations. Furthermore, in sequences that include variations in illumination, our likelihood model usually performs better than the default tracker likelihood model.

In-Use Performance and Durability of Particle Filters on Heavy-Duty Diesel Trucks.

Diesel particle filters (DPFs) are standard equipment on heavy-duty diesel trucks with 2007 and newer engines in the U.S. This study evaluates the performance and durability of these filters. Black carbon (BC) emission rates from several thousand heavy-duty trucks were measured at the Port of Oakland and Caldecott Tunnel over multiple years as California regulations accelerated the adoption of DPFs. As DPF use increased, fleet-average BC emissions decreased, and emission factor distributions became more skewed. Relative to 2004-2006 engines without filters, DPFs reduced BC emission rates by 65-70% for 2007-2009 engines and by >90% for 2010+ engines. Average BC emission rates for 2007-2009 engines increased by 50-67% in 2015 relative to measurements made 1-2 years earlier. Some trucks in this cohort have become high-emitters, indicating that some DPFs are no longer working well. At the Port, where DPFs were universal in 2015, high-emitting 2007-2009 engines (defined here as emitting >1 g BC kg-1) comprised 7% of the fleet but were responsible for 65% of the total BC emitted. These observations raise concerns about DPF durability and the prospects for fully mitigating adverse effects of diesel particulate matter on human health and the environment.

Particles resampling scheme using regularized optimal transport for sequential Monte Carlo filters

SummaryA resampling method is presented for improving the performance of particle filters by using adaptive numbers of the resampling particles. The proposed method replaces the resampling step of a sequential importance resampling particle filter with regularized optimal transport that makes use of the transport plan to warp a posteriori weights into desired weights. The basis of the method is the same as an ensemble transform particle filter that uses the linear transformation step in the resampling of particles. However, the linear transformation step in ensemble transform particle filter is computationally expensive and needs relaxation matching between the two samples for robust processing. Furthermore, its performance can be degraded if the number of the effective particles becomes below the required number of particles, which represents the needed particle number of importance sampling. Therefore, in this paper, a regularized optimal transport method is used to deal with computational load reduction and relaxation matching in the resampling step by using a modified Sinkhorn‐Knopp algorithm that considers the process iteration rate convergence. In order to maintain the proper number of the effective particles for states estimation, Kullback‐Leibler distance sampling method is also implemented in the resampling process. The proposed method is evaluated by theoretical analysis including simple simulations compared with the conventional resampling methods. According to the simulations, the proposed method indicates that estimation accuracy is better than conventional methods with small number of the effective particles.

Improving Particle Filter Performance by Smoothing Observations

AbstractThis article shows that increasing the observation variance at small scales can reduce the ensemble size required to avoid collapse in particle filtering of spatially extended dynamics and improve the resulting uncertainty quantification at large scales. Particle filter weights depend on how well ensemble members agree with observations, and collapse occurs when a few ensemble members receive most of the weight. Collapse causes catastrophic variance underestimation. Increasing small-scale variance in the observation error model reduces the incidence of collapse by de-emphasizing small-scale differences between the ensemble members and the observations. Doing so smooths the posterior mean, though it does not smooth the individual ensemble members. Two options for implementing the proposed observation error model are described. Taking a discretized elliptic differential operator as an observation error covariance matrix provides the desired property of a spectrum that grows in the approach to small scales. This choice also introduces structure exploitable by scalable computation techniques, including multigrid solvers and multiresolution approximations to the corresponding integral operator. Alternatively the observations can be smoothed and then assimilated under the assumption of independent errors, which is equivalent to assuming large errors at small scales. The method is demonstrated on a linear stochastic partial differential equation, where it significantly reduces the occurrence of particle filter collapse while maintaining accuracy. It also improves continuous ranked probability scores by as much as 25%, indicating that the weighted ensemble more accurately represents the true distribution. The method is compatible with other techniques for improving the performance of particle filters.

Simplified partial resampling method for state estimation usingparticle filter

Particle filter methods are used in the estimation and tracking of the objects for non-linear and non-gaussian noise conditions. In this paper work the object estimation using partial resampling methods are discussed. On using partial resampling method resampling becomes faster. The performance of particle filter with partial resampling scheme is analyzed using the state estima-tion of a simple pendulum.

Chaotic particle filter for visual object tracking

In this paper, a chaotic particle filter method is introduced to improve the performance of particle filter based on chaos theory. The methodology of the algorithm includes two steps. First, the global motion estimation is used to predict target position using dynamical information of object movement over frames. Then, the color-based particle filter method is employed in the local region obtained from global motion estimation to localize the target. The algorithm significantly reduces the number of particles, search space, and the filter divergence because of high-order estimation. To verify the efficiency of the tracker, the proposed method is applied to two datasets, consisting of particle filter-based methods under the Bonn Benchmark on Tracking (BoBoT), the large Tracking Benchmark (TB), and Visual Object Tracking (VOT2014). The results demonstrate that the chaotic particle filter method outperforms other state-of-the-art methods on the abrupt motion, occlusion, and out of view. The precision of the proposed method is about 10% higher than that of other particle filter algorithms with low computational cost.

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

ABSTRACTParticle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.

Experimental measurement of the filtration efficiency and pressure drop of wall-flow diesel particulate filters (DPF) made of biomorphic Silicon Carbide using laboratory generated particles

Biomorphic Silicon Carbide (bioSiC) has been recently introduced in the scope of porous ceramic substrates for hot gas filtration applications, where it has demonstrated to have good thermal and mechanical properties, and a high potential to meet the requirements for current Diesel Particulate Filters (DPF). In this experimental study, a small wall-flow bioSiC diesel filter was characterized using a soot generator, the particle size distribution of which being similar to the one generated by a diesel engine. The bioSiC samples were manufactured from Medium Density Fiberboard (MDF) following a general manufacturing procedure for bioSiC ceramics, but paying special attention in the mechanizing stage to the geometry and optimal design of the honeycomb structure required for diesel engine applications. The samples had a cell density of 57.59 cell/cm2 (371.6 cpsi), a square cross section of 9.2 × 9.2 mm, and a length of 31 mm. To generate the particle laden stream and perform the filtration tests, a synthetic Soot Generator (SG) was used. Tests were performed under controlled and reproducible conditions, with a fixed gas flow rate of 5 LPM and a soot mass flow rate of 4 mg/h. The filtration efficiency was determined with the aid of a Scanning Mobility Particle Sizer (SMPS) from the measurements of the particle concentration upstream and downstream the filter samples. During the soot loading process, the pressure drop was also monitored. The results show that, in the initial stage (clean filter), bioSiC wall-flow DPFs may have a filtration efficiency between 0.7 and 0.85 and a pressure drop of around 2 kPa for a normalized wall velocity of 0.01 m/s at ambient temperature. The filtration performance of wall-flow bioSiC particle filters showed in this work can help us to better understand their real potential for automotive applications.

Huber cubature particle filter and online state estimation

The cubature particle filter, which utilizes the third-degree spherical–radial cubature rule to generate proposal distribution, performs inaccurately when the measurement noises deviate from the Gaussian probability distribution. To improve the performance of cubature particle filter, the update process is recast by making use of the Huber technique which could generate robustness with respect to deviations from the assumed Gaussian noise. Moreover, the general resampling process is replaced by fine variety resampling approach, and as a result the degeneracy phenomenon is effectively reduced. The improved state estimation algorithm is named as Huber cubature particle filter (HCPF). The effectiveness of the proposed algorithm is illustrated via an aircraft actuator model and the experimental results are provided to support the theoretical results.

Parallel implementation of color-based particle filter for object tracking in embedded systems

Recently, embedded systems have become popular because of the rising demand for portable, low-power devices. A common task for these devices is object tracking, which is an essential part of various applications. Until now, object tracking in video sequences remains a challenging problem because of the visual properties of objects and their surrounding environments. Among the common approaches, particle filter has been proven effective in dealing with difficulties in object tracking. In this research, we develop a particle filter based object tracking method using color distributions of video frames as features, and deploy it in an embedded system. Because particle filter is a high-complexity algorithm, we utilize computing power of embedded systems by implementing a parallel version of the algorithm. The experimental results show that parallelization can enhance the performance of particle filter when deployed in embedded systems.