Development Of Particle Filter Research Articles

Auxiliary Particle Filter for Prognostics and Health Management

Accurately predicting the remaining useful life (RUL) of a system is a crucial factor in prognostics and health management (PHM). This paper introduces an auxiliary particle filter (APF) model, which has the advantages of dynamically updating the model parameters and being optimized in computational speed for prognosis applications in real engineering problems. The development of particle filter (PF) in the recent decade focused on increasing the PF model’s complexity to solve more difficult problems. However, the added complexity negatively impacts the computational speed. The number of particles is commonly reduced to compensate for this increased computational burden, but this significantly reduces the accuracy of PF’s posterior distribution. The developed APF model can estimate unknown states and model parameters at the same time with a large number of particles. This algorithm was demonstrated with a dataset from an electric motor accelerated aging experiment. The results show that this model can quickly and accurately predict the RUL and is robust to measurement noise.

A Polytopic Box Particle Filter for state estimation of Non Linear Discrete-Time Systems

The development of Particle Filters has made possible state estimation of dynamic systems presenting non-linear dynamics and potential multi-modalities. However, the efficiency of these approaches depends tightly of the required number of particles which may prove very high to approximate large range of uncertainty on the process or the measurements. To overcome this issue, the Box-Particle Filter (BPF) combines the versatility of the Particle Filter and the robustness of set-membership algorithms. The particles are replaced by boxes which represent in a compact way large variations of the estimates. Although this filter presents various advantages and requires a small number of boxes to estimate the state, the resulting estimates may prove pessimistic, as the uncertainty description as unions of axis-aligned intervals can be rather rough and doesn’t account for potential dependencies between the resulting estimate components. In the proposed paper, a new version of the BPF is proposed. Boxes are replaced by polytopes (multidimensional polygons) in the filter algorithm, so that they can adapt to represent state components dependency. This modification tends to ameliorate the estimation precision (i.e. the size of the final set that includes the true state decreases) while keeping the number of required polyhedrons small. Several examples illustrate the benefits of such an approach.

Adaptive memory-based single distribution resampling for particle filter

The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices’ memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices’ physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle’s particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

Overview of Improved Particle Filter Based on Integrated Navigation System

Particle filter has some advantages in dealing with the problems of the state equation nonlinear and noise distribution non Gauss in Integrated Navigation System. The summary for improved method of particle filter would be beneficial to study the application of particle filter in integrated navigation field deeply and overcome the problems of particle degeneracy and sample impoverishment with particle filtering. The basic algorithm and theory of particle filter and the reasons of particle degeneracy are expounded and the development of particle filter at home and abroad is given, then a summary for different methods to improve the performance of particle filter (including increasing particle number, resampling technology, selecting the best importance density function, and improving particle filter based on Neural Network). Several improved methods can effectively improve the filtering performance and improve positioning accuracy, in the actual situation, according to different conditions of use to choose the appropriate method of improvement.