Accuracy Of Particle Filter Research Articles

Research and Application of Target Tracking Algorithm in Ship Safety

Target tracking is often used in tasks such as video surveillance. Its purpose is to obtain the target of interest in the sequence image accurately, robustly and in real time, and to establish the connection between the moving objects in each image. In recent years, with the continuous development of the global shipping economy, ships have gradually developed into large-scale and high-speed, and how to ensure the safety of ships sailing in these sports has become an important issue. Based on the principle of particle filter and mean shift algorithm, this paper takes the moving ship as the research object and uses the polynomial fitting method to analyse the performance of the two algorithms. A large number of experimental results show that the mean shift algorithm has higher accuracy, and the particle filter method has better real-time performance. And as the number of particles increases, the accuracy of particle filtering will gradually increase.

Unsupervised Learning Grouping-Based Resampling for Particle Filters

Conventional resampling for particle filters suffers from discarding much potentially useful information due to using less information of spatial distribution of sampling particles set. An unsupervised learning grouping based resampling for particles filter, which could further exploits the relevant spatial information hidden in sampling particles set, was proposed. The core of our approach is that the k-means clustering algorithm is used to automatically form clusters of similar particles, instead of simple heuristic partitioning of the particles set in the spatial domain. Based on the time series analysis between the particle path and observation path for all particles in each cluster, the particle called important particle that has highest temporal correlation is chosen. The diversity of resampling particles set is strengthened by adding important particles to improve the residual resampling. Additionally, by the Kolmogorov-Smirnov two-sample test, we proof that the particles set resampled by the proposed algorithm and the original particles set come from the same distribution function. Extensive experimental results on two numerical models show that the estimation accuracy of particle filter can be improved in this way.

Predictive accuracy of particle filtering in dynamic models supporting outbreak projections

BackgroundWhile a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations.MethodsCombining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve.ResultsWe observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak.ConclusionCombining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.

Efficient Particle Swarm Optimized Particle Filter Based Improved Multiple Model Tracking Algorithm

To meet the requirements of modern radar maneuvering target tracking system and remedy the defects of interacting multiple model based on particle filter, noninteracting multiple model (NIMM) and enhanced particle swarm optimized particle filter (EPSO‐PF) are proposed. The improved maneuvering target tracking algorithm (NIMM‐EPSO‐PF) in this article combines the advantages of NIMM with those of EPSO‐PF. NIMM is used to figure out the index of particles to avoid the high computing complexity resulting from particle interaction, and EPSO‐PF can not only improve the equation of particle update through the rules individuals develop an understanding of group but also enhance particle diversity and accuracy of particle filter through the small variation probability of superior velocity. Besides, the random assignment of inferior velocity is capable of upgrading filter efficiency. As shown by the experimental result, the NIMM‐EPSO‐PF not only improves target tracking accuracy but also maintains high real‐time performance. Therefore, the improved algorithm can be applied to modern radar maneuvering target tracking field efficiently.

Anadvanced integrated framework for moving object tracking

This paper first introduces the concept of a geogram that captures richer features to represent the objects. The spatiogram contains some moments upon the coordinates of the pixels corresponding to each bin, while the geogram contains information about the perimeter of grouped regions in addition to features in the spatiogram. Then we consider that a convergence process of mean shift is divided into obvious dynamic and steady states, and introduce a hybrid technique of feature description, to control the convergence process. Also, we propose a spline resampling to control the balance between computational cost and accuracy of particle filtering. Finally, we propose a boosting-refining approach, which is boosting the particles positioned in the ill-posed condition instead of eliminating the ill-posed particles, to refine the particles. It enables the estimation of the object state to obtain high accuracy. Experimental results show that our approach has promising discriminative capability in comparison with the state-of-the-art approaches.

Ensemble unscented Kalman filter for state inference in continuous–discrete systems

The authors consider non-linear state filtering problem in continuous–discrete systems, where the system dynamics is modelled by a stochastic differential equation, and noisy measurements of the system are obtained at discrete time instances. A novel particle method is proposed based on sequential importance sampling. This approach uses a bank of the continuous–discrete unscented Kalman filters (CDUKFs) to obtain the importance proposal distribution, retaining the advantage of the CDUKF in continuous–discrete systems as well as the accuracy of particle filter in highly non-linear systems. Simulation results show that the algorithm outperforms some other benchmarks substantially in estimation accuracy.

Robust Adaptive Unscented Particle Filter

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.