Improved Particle Filter Algorithm Research Articles

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.

Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf

Abstract Aiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor) propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF) was presented, and it was imported into the marine SSP propulsion motor control system. The strong tracking filter was used to update particles in the new algorithm and produce importance densities. As a result, the problems of particle degeneracy and sample impoverishment were ameliorated, the propulsion motor states and the rotor resistance were estimated simultaneously using strong track filter (STF), and the tracking ability of marine SSP propulsion motor control system was improved. Simulation result shown that the improved EPF algorithm was not only improving the prediction accuracy of the motor states and the rotor resistance, but also it can satisfy the requirement of navigation in harbor. It had the better accuracy than EPF algorithm.

Urban Road Traffic State Identification Algorithm Based On Particle Filter and Fuzzy Discrimination

Urban road traffic state identification is a key link to realize the intelligent transportation based on the Internet of Vehicles, and accurately positioning vehicles is the foundation to realize the traffic state identification. Aiming at the problem that GPS has signal blind area in positioning vehicles, a vehicle positioning algorithm based on particle filter was proposed, it could improve the traditional algorithm on degradation and large amount of calculations; Based on vehicle positioning, an urban road traffic state identification algorithm based on fuzzy discrimination was proposed, it could comprehensively consider multiple factors’ influence on traffic state. The experiment results show that the improved particle filter algorithm’s mean squared error has increased about 55.437% compared with GPS method, and the traffic state identification algorithm can accurately identify the traffic state of the study area, it can prove that the urban road traffic state identification algorithm based on particle filter and fuzzy discrimination is feasible and effective.

An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method

Data assimilation (DA) has developed into an important method in Earth science research due to its capability of combining model dynamics and observations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions. Thus, it is now receiving increasing attention in DA. However, the particle degeneracy still remains a major problem in practical application of PF. In this paper, an improved PF is proposed based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) method. It uses an EnKF analysis to define the proposal density of PF instead of the prior density, thus reducing the risk of particle degeneracy. Furthermore, when particle degeneracy happens, resampling is performed follow by an MCMC move step to increase the diversity of particles, thus reducing the potential of particle impoverishment and improving the accuracy of the filter. Finally, the improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture in the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can provide more accurate assimilation results and also need fewer particles to get reliable estimations than the EnKF and the standard PF, thus demonstrating the effectiveness and practicality of the improved PF.

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

Research on Detection and Trackingof Player in Broadcast Sports Video

The paper presents a method which bases on support vector machine(SVM) and particle filtering for detecting and tracking player in the broadcast sports video. Firstly, through the combination of Support Vector Classification and CourtSegmentationmethod, it proposes the algorithm for examining automatically members in those videos, which is used to initialize the trace of subsequent visual objects; secondly, by combing support vector regression frame and the one of sequential Monte Carlo, it brings forth the improved particle filtering algorithm which is applied to follow visual objects, enabling the traditional particle filtering method to achieve robust trail of such targets even when the particle set is small, together with effective enhancement of the running efficiency of tracking system.

A new tracking approach for visible and infrared sequences based on tracking-before-fusion

Traditional target tracking algorithms based on single sensor images are not applicable in some circumstances such as cross-background, similar appearance object, shadow and occlusion with nonlinear and random characteristic. Previous image fusion tracking approach has requirement for precise registration. The traditional fusion-before-tracking strategy suffers from several serious drawbacks such as mutual influence between different single sensor resulting in the loss of the own advantages. The current proposed approach using tracking-before-fusion strategy operates in five ways. Firstly, the specified target is tracked using improved particle filter algorithm in visible sequence. Secondly, tracking in infrared sequence is based on improved template matching scheme. Thirdly, an un-registered treatment scheme is applied for un-registered sequences. Fourthly, the tracking results from visible and infrared sequences are fused by the new fusion rule. Finally, each result is fed back and updated for tracking improvement. The tracking experiments indicate that the proposed approach has higher efficient, robustness and real-time capability.

GPS Receiver Autonomous Integrity Monitoring Algorithm Based on Improved Particle Filter

Reliability of a navigation system is one of great importance for navigation purposes. Therefore, an integrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. According to the characteristic of GPS (Global Positioning System) receiver noise distribution and particle degeneracy and sample impoverishment problem in particle filter, an improved particle filter algorithm based on genetic algorithm for detecting satellite failures is proposed. The combination of the re-sampling method based on genetic algorithm and basic particle filter is used for GPS receiver autonomous integrity monitoring (RAIM). Dealing with the low weight particles on the basis of the genetic operation, genetic algorithm is used to classify the particles. It brings the selection, crossover and mutation operation in genetic algorithm into the basis particle filter .The method for detecting satellite failures which affect only subsets of system measurement. In addition to a main particle filter, which processes all the measurements to give the optimal state estimate, a bank of auxiliary particle filters is also used, which process subsets of the measurements to provide the state estimates which serve as failure detection references. The consistency of test statistics for detection and isolation of satellite fault is established. The failure detection is undertaken by checking the system state logarithmic likelihood ratio (LLR). The RAIM algorithm combined the genetic particle filter and the likelihood method is illustrated in detail. Experimental results based on GPS real raw data demonstrate that the algorithm under the condition of non- Gaussian measurement noise can improve the accuracy of state estimation, effectively detect and isolate fault satellite, improve the performance of the fault detection. Experimental results demonstrate that the proposed approach is available and effective for GPS RAIM.

IMM Iterated Extended Particle Filter Algorithm

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

An Improved Particle Filter Algorithm and Its Performance Analysis

An Improved Particle Filter Algorithm and Its Performance Analysis

Niche PSO Particle Filter with Particles Fusion for Target Tracking

An improved particle filter algorithm is proposed to track a randomly moving target in video. In particle filter framework, a particle swarm optimization improved by niche technique which implemented by restricted competition selection is integrated. It can move particles into high likelihood area of target and form multi-population distribution, so that the searching capability of particles is enhanced and then the adaptation to the change of dynamic target state is improved. The particles of niching particle swarm optimization and the particles of particle filter are integrated for new particle weight calculation and finally realize a new particle filter for target tracking in video sequence.

Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.

An Adaptive Motion Model and Multi-feature Cues Based on Particle Filter for Object Tracking

If there is an occlusion, the target state model would not match motion model anymore and measurement model also would get worse. To solve these problems, an improved particle filtering algorithm based on adaptive motion model and multiple-cue fusion is presented. Under the theory framework of particle filters, the weighted color histogram and LBP texture feature entropy are used to describe features. And the algorithm adjusts features distribution coefficient automatically by calculating the Bhattacharyya distance between the object reference distribution and object sample distribution, thus the color and texture features can intelligently be fused to develop the observation model. The simple second order auto regressive model is chosen as the state transition model, and the system noise variance is adaptively determined by the minimum noise variance of every feature in object tracking. For the occlusion problem, the system maximum noise variance can be selected, along with particle random motion intensified and disseminating coverage amplified. The posterior distribution of the object is approximated by a set of weighted samples, while object tracking is implemented by the Bayesian propagation of the sample set. The analyses and experiments show that the performance of the proposed method is more effective and robust to target maneuver and occlusion and has good performances under complex background.

An improved particle filter algorithm based on Markov Random Field modeling in stationary wavelet domain for SAR image despeckling

Particle filter (PF) is an effective approach to nonlinear and non-Gaussian Bayesian state estimation and has been successfully applied to wavelet-based synthetic aperture radar (SAR) image despeckling. In this paper, we propose an improved PF despeckling algorithm based on Markov Random Field (MRF) model that can preserve the edge, textural information and structural features of SAR images well. First, we show that the wavelet coefficients of SAR images which exhibit significantly non-Gaussian statistics can be described accurately by generalized Gaussian distribution (GGD) in stationary wavelet domain. Secondly, to amend the weight deviation, MRF model parameters are introduced to redefine the importance weight of the particles. At last, region-divided processing is implemented for the real time application of the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated images and real SAR images.

Effect of different construction materials on propagation of GPS monitoring signals

The GPS carrier phase observations are widely used for the high precision static and kinematic positioning applications. However, GPS signals are seriously distorted when passing through walls and other obstructions. This paper focuses on this issue and outlines the research carried out to investigate the effects of some commonly used construction materials on the GPS signals. For the purpose of generating the multipath in a controlled manner, an experimental set-up is designed to test the effect of typical building surface materials (toughened glass, wood board, PVC board and ceramic tile) on positioning accuracy. The effects of signal attenuation on the accuracy of the positioning solution are explored by an improved particle filtering algorithm and some statistical methods, and reasons for these effects are further analyzed. The findings of the experimental results may be used to enhance the performance of GPS technology.

Case study and proofs of ant colony optimisation improved particle filter algorithm

Particle filters (PF), as a kind of non-linear/non-Gaussian estimation method, are suffering from two problems in large-dimensional cases, namely particle impoverishment and sample size dependency. Previous studies from the authors have proposed a novel PF algorithm that incorporates ant colony optimisation (PFACO), to alleviate these problems. In this paper the authors will provide a theoretical foundation of this new algorithm; two theorems are introduced to validate that the PFACO introduces smaller Kullback–Leibler divergence (K–L divergence) between the proposal distribution and the optimal one compared to those produced by the generic PF. In addition, with the same threshold level, the PFACO has a higher probability than the generic PF to achieve a certain K–L divergence. A mobile robot localisation experiment is applied to examine the performance between various PF schemes.

A method of 2D defect profile reconstruction from magnetic flux leakage signals based on improved particle filter

Defect profile reconstruction in magnetic flux leakage (MFL) testing means the reconstruction of defect profiles and parameters according to inspected MFL signals, and it is the key point in realising MFL inversion. In this paper, the inversion problem is described as a classical discrete-time tracking problem based on state and measurement equations. A state-space approach is adopted to solve the inversion problem by establishing the state-space model of defect profile and MFL signals, and apply the improved particle filter algorithm to the defect profile reconstruction. The results indicate that the improved particle filter-based inversion algorithm is an effective and feasible new method of MFL inversion due to its accuracy and robustness against noise. Meanwhile, the improved resampling algorithm further improves the efficiency.

Visual tracking by particle filtering in a dynamic environment

An improved particle filter algorithm was presented to track a moving target under natural environment, in which the colour histogram was integrated with the measurement model, the latest observations were taken into account and the system model is the second order autoregressive process. The proposed method can deal with rotation and scale transformation, variations in the light source and partial occlusions and the tracked target can be rigid or non-rigid. The demonstration examples showed that the method can track the target with robustness in real-time.

Compensation of GPS non-Gaussian pseudorange error based on improved particle filter algorithm

Compensation of GPS non-Gaussian pseudorange error based on improved particle filter algorithm

An improved particle filtering algorithm based on observation inversion optimal sampling

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particle filter, an improved particle filtering algorithm based on observation inversion optimal sampling was proposed. Firstly, virtual observations were generated from the latest observation, and two sampling strategies were presented. Then, the previous time particles were sampled by utilizing the function inversion relationship between observation and system state. Finally, the current time particles were generated on the basis of the previous time particles and the system one-step state transition model. By the above method, sampling particles can make full use of the latest observation information and the priori modeling information, so that they further approximate the true state. The theoretical analysis and experimental results show that the new algorithm filtering accuracy and real-time outperform obviously the standard particle filter, the extended Kalman particle filter and the unscented particle filter.