Problem Of Particle Degradation Research Articles

Blind separation of PCMA signals based on improved particle filter algorithm

For the problem of particle degradation and depletion in the blind separation of particle filter algorithm, particle swarm optimization algorithm and genetic algorithm are used to improve the traditional PCMA blind separation algorithm based on particle filter. The improved algorithm uses particle swarm optimization algorithm to optimize the particle and uses genetic algorithm to resample the particle. It can maintain the diversity of particles and reduce the problem of particle degradation and dilution effectively. The simulation shows that the improved blind separation algorithm has better separation performance.

Positioning optimisation based on particle quality prediction in wireless sensor networks

The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

Research on Simultaneous Location and Mapping Algorithm of Intelligent vehicle Based on Improved Particle Filter Resampling

For the estimation problem that the Simultaneous Location and Mapping algorithm used in Intelligent vehicle suffers from sample impoverishment, a Multi-threshold differential evolutionary particle resampling method is proposed. The method adopts different particle updating Strategys according to different thresholds of effective particle numbers. particle diversity will be rapidly improved by differential evolution when particle degradation is severe. When the particle degradation is light, the system resampling method is directly used to copy the large weight particles and eliminate the small weight particles. By adopting different particle regeneration strategies for particles with different degrees of degradation, the problem of particle degradation and depletion in the process of state estimation can be effectively solved. Simulation experiments show that the root mean square error of the improved algorithm pose is reduced by 2.592m compared with the traditional algorithm. The road sign estimation root mean square error is also reduced by 2.428m compared with the traditional algorithm. Experiments show that the multi-threshold differential particle filter resampling method can effectively improve the particle diversity and ensure the state estimation accuracy of the vehicle.

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods.

The attitude inversion method of geostationary satellites based on unscented particle filter

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.

An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis

In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.

Time-Dependent Reliability Prediction of Bridge Member Based on MGPF and SHM Data

In the long-term monitoring period, the structural health monitoring (SHM) system produces a huge amount of monitoring data. It becomes a very important thing that how to real-timely predict structural reliability indices from such huge number of monitored data. In this paper, To real-timely predict reliability of bridge members with real-time monitoring information, with the long-term mass monitored data of health monitoring system, the data-based dynamic model including observation equation and state equation is built, and then the mixed Gaussian particle filter (MGPF) is introduced. With particle filter method, Bayesian method and dynamic model, the posteriori distribution parameters of state variable and one-step forward prediction distribution parameters of monitored data are predicted. Through resampling technique, with MGPF, the prediction precision of dynamic model can generally increase. Based on the dynamic monitored data, the weights of resampled particles can be constantly updated. Therefore the problem of particle degradation is solved. Finally based on the real-time predicted distribution parameters, with the first order second moment (FOSM) method, the dynamic reliability of bridge member is predicted, and an actual example is provided to illustrate the application and feasibility of the proposed models and methods.

Particle Filter Target Tracking Algorithm Based on MCMC Iteration Cubature

In view of the problem of particle degradation and tracking accuracy in the standard particle filter tracking target algorithm, a new improved particle filter algorithm called Iterated Cubature Kalman Particle Filter (ICKPF) is proposed in this paper. The new ICKPF algorithm is based on the Markov Chain Monte Carlo (MCMC), and the cubature rule based on numerical integration method is used to calculate the mean and covariance, which generates the proposal distribution for the particle filter. The current measurements are integrated into the proposal distribution. Therefore, degree of approximation to the system posterior density is improved. Simulation results show that the estimation error of the ICKPF-MCMC algorithm is much better than other algorithms.