Traditional Particle Filter Research Articles

Application of Particle Filter Algorithm Based on Gaussian Clustering in Dynamic Target Tracking

In order to solve the problem of particle depletion and complexity in traditional particle filtering, a particle filter target tracking method based on Gaussian clustering is proposed in this paper. Combined with adaptive double-sampling and gradmethod, the real time and robustness of dynamic tracking are improved significantly.

Activated sludge process faults diagnosis based on an improved particle filter algorithm

Maintaining the discharge standards of biologically-treated wastewater is essential in order to protect receiving water bodies from secondary pollution. If unexpected faults happen in an activated sludge process that is used as biological treatment, it would affect the quality of the receiving water body. Thus, in this paper, an improved particle filter (PF) algorithm, based on a variable frequency mutation (VFM) strategy is proposed for the process faults diagnosis. This is inspired by the frequency conversion for energy-saving application in the industrial process; an adaptive frequency conversion operator has been incorporated into the mutation operation of immune algorithm to reduce the system operating costs. Then, the resampling process of PF algorithm was replaced by particle mutation based on the previously calculated information for securing the diversity and the effectiveness of the particle; lastly, a VFM-based PF algorithm for system states estimation and fault diagnosis was established. This algorithm not only effectively increases the adaptability of the particle to changes of the system state, but also conductively solves the problems of the degeneracy in the traditional PF algorithm and the diversity weakening caused by resampling operation. Simulation results show that the algorithm can effectively improve the estimation accuracy of the nonlinear system states. The application results of faults diagnosis in the activated sludge process show that it can accurately diagnose the occurrence of faults. Therefore, the proposed method has great practical significance in wastewater treatment plants to avoid problems caused by unexpected faults.

Underwater Pure Orientation Target Tracking Based on Adaptive Box Length KLD PF Algorithm

Aiming at the characteristics of underwater pure orientation target tracking, an adaptive box length Kullback-Leibler Distance (KLD) Particle Filter (PF) algorithm is proposed based on traditional adaptive KLD PF algorithm. When performing underwater pure orientation target tracking, the traditional adaptive KLD sampling may have a problem that the filtering accuracy is reduced or even invalid due to the fixed length of the KLD box. In the algorithm proposed in this paper, the length of the KLD box can be automatically adjusted according to the current particle distribution range, which can well solve the problem of precision degradation caused by the fixed KLD box length of the traditional adaptive KLD algorithm. The effectiveness of the adaptive box length KLD PF algorithm is verified by comparison with the traditional KLD PF algorithm in simulation. In this simulation, the adaptive box length KLD PF algorithm can effectively adapt to the changing environment of underwater tracking and has good tracking performance.

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.

Object tracking using the particle filter optimised by the improved artificial fish swarm algorithm

In particle filter algorithm, the weight values of particles will gradually decrease as the increase of iteration times and the variance of the weight value of the particles will increase. This will lead to an increase in the deviation between the estimated state and the true state. In order to deal with this problem, an improved particle filter algorithm is proposed in this paper. That is, an improved artificial fish swarm optimisation algorithm is used to optimise the traditional particle filter. In the improved particle filter algorithm, the resampled particles will be driven to the region with high likelihood function to increase the weight values of the particles. Thus, the estimated state is closer to the real state. Experiment results show the advantage of our new algorithm over a range of existing algorithms.

A modified particle filter for parameter identification with unknown inputs

Particle filter (PF) is usually used for identifying structural parameters in nonlinear systems. However, the traditional PF method requires that all the external excitations are available or can be measured, which may not be the case for many structures. In this paper, a modified PF method with unknown inputs, referred to as PF-UI, is proposed to identify the augmented states, including structural states and parameters, as well as the unknown excitation inputs. The augmented states are identified by the traditional PF algorithm, and the unknown excitations are simultaneous identified with the least-square algorithm. Such an analytical solution for PF-UI is not available in the previous literature. Numerical studies of two typical nonlinear systems are conducted to demonstrate that the proposed approach is capable of identifying the states and parameters with unknown excitation inputs.

Dim small target tracking based on improved particle filter

As to solve the problem of dim small target tracking in low signal-to-noise ratio (SNR<3 dB) scenes, an improved particle filter tracking method is proposed. This paper firstly obtains the gray feature by spatial position weighting method, and combines the neighborhood motion model and the gray probability graph to get the motion features of dim small target. Then construct the joint observation model of gray and motion features to calculate the particle weights. At the same time, in the process of tracking, the gray distribution of the target is not stable, and the strategy of adaptively updating the gray template of reference target is added. Finally, the sequence image is used to prove the tracking effect of dim small target. Experiments show that compared with the traditional particle filter algorithm, the proposed method greatly enhanced the tracking ability of dim small target in low SNR (SNR<3 dB) scenes.

Localization of Indoor Mobile Robot Using Minimum Variance Unbiased FIR Filter

The demand of indoor localization has recently grown quickly in industries. In general, a localization system is required to be reliable, fast, and have high accuracy. In this paper, the ultrawideband (UWB) technique is combined with the inertial navigation sensor (INS) to form a coupled UWB/INS localization framework, which inherits the advantages from both components. A minimum variance unbiased finite impulse response (MVU FIR) method is then applied to obtain accurate position and velocity estimations from noisy measurements. Two experiments and several simulations are conducted. Compared with the traditional Kalman filter (KF) and particle filter, the MVU FIR filter exhibits better immunity to the errors about a priori knowledge of noise variances. It can handle the kidnapped problem, and recover from some extreme failures satisfactorily. Moreover, the MVU FIR filtering algorithm is fast and easily implementable. Its online computational time is even lower than that of the KF, which is favorable in localization applications. Note to Practitioners —For indoor robot localization, an effective filtering algorithm can improve the accuracy significantly. Existing and commonly used filtering approaches suffer from weak robustness to the imprecise a priori knowledge of noise variances used, unsatisfied performance when dealing with sudden behaviors of robot, and large online computational load. This paper suggests a new practical method to estimate the current state using finite past measurements with a finite impulse response filter gain computed off-line. By testing it within the coupled ultrawideband/inertial navigation sensor localization framework under different operating conditions, we demonstrate that the proposed method can effectively overcome the above problems, providing fast, accurate, and reliable estimation in a practical environment.

Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

Prognosis and remaining useful life (RUL) estimation of components and systems (C&amp;S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared with the one-step traditional particle filter that depended on the first-order state equation. However, despite this improvement, the need to enhance the accuracy of fault prognosis, diagnosis and detection cannot be overemphasized. To this end, an optimal multi-level particle filter (OPMPF) algorithm that combines genetic algorithm (GA) optimization and multi-level particle filter (MPF) techniques is used to predict the RUL of the C&amp;S in order to enhance the accuracy of the estimation at different forms of deterioration in operation. A 9-fold cross-validation ensemble MPF that utilized lithium-ion (Li+) batteries’ charge capacity decay to test the developed OPMPF algorithm showed an improvement of over 200% in the estimated RUL when compared with the TRMPF estimation.

Pedestrian Tracking Utilizing Scale Invariant Feature Transform and Particle Filter

Background: Pedestrians are the major road users in transportation system. They are more vulnerable than other road users when traffic accidents occurr, which has attracted much concerns from researchers around the world by developing corresponding countermeasures. Pedestrians are not easy to be tracked accurately because of the changes in illumination conditions and the occlusion of human body using traditional tracking algorithms. Method: To improve the effectiveness of pedestrian tracking, particle filter (PF) is utilized to track the pedestrian, which is detected using the histograms of oriented gradient (HOG) features. Then scale invariant feature transform (SIFT) features are employed to represent the region of interest for sequence images. Result: The representative vector utilized to describe the pedestrian is renewed after comparing the object model and the characteristic variables during the tracking process. This method takes advantage of color histogram and adopts PF to predict the position of the pedestrian. Conclusion: Experiments were conducted to compare the proposed method with traditional PF tracking method. Results verify the accuracy and efficiency of the proposed method. Keywords: Histograms of oriented gradient, scale invariant feature transform, color histogram, particle filter, pedestrian tracking.

A dual channel perturbation particle filter algorithm based on GPU acceleration

The particle filter (PF) algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to one-dimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit (GPU) parallel computing the platform, the “channel-level” and “particlelevel” parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

Passively Track WiFi Users With an Enhanced Particle Filter Using Power-Based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. In this paper, we provide a passive tracking system for WiFi signals with an enhanced range-only particle filter using fine-grained power. Our proposed particle filter, WVT-bootstrap particle filter, provides improved observation likelihood and is equipped with a single coordinated turn model to address the challenges in passive positioning. The anchor nodes for WiFi signal sniffing use software defined radio techniques to extract channel state information for multipath mitigation and a non-linear regression method is used for the path-loss model. Our tracking system produces measured positioning errors that, in the 80th percentile, are equal to or less than 2 m; this represents a 33% improvement over the traditional bootstrap particle filter. Additionally, it requires (0.12 s for 1000 particles) only half of the computation efforts as a multi-model particle filter.

A New PHD Algorithm in Unknown Clutter Environment Based on Box Particle

In unknown clutter environment, traditional Probability Hypothesis Density (PHD) filter in multi-target tracking cannot guarantee a good performance and multitude number of particles leads to time consuming and low efficiency. Aiming at the problems, a new PHD filter tracking algorithm in unknown clutter environment based on interval analysis was proposed. Firstly, radar targets and clutter disjoint union state space modeled were established in random finite set. Next, using measurement model set up clutter model and derived to multi-target updated state function based on box particles. Additionally, the state of multi-target was recursively estimated in utilization of PHD filter box particles. Simulation reveals that the proposed algorithm is able to dramatically lower computational time with better tracking performance compared with traditional box particle filter.

Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

AbstractIn this study, a copula‐based particle filter (CopPF) approach was developed for sequential hydrological data assimilation by considering parameter correlation structures. In CopPF, multivariate copulas are proposed to reflect parameter interdependence before the resampling procedure with new particles then being sampled from the obtained copulas. Such a process can overcome both particle degeneration and sample impoverishment. The applicability of CopPF is illustrated with three case studies using a two‐parameter simplified model and two conceptual hydrologic models. The results for the simplified model indicate that model parameters are highly correlated in the data assimilation process, suggesting a demand for full description of their dependence structure. Synthetic experiments on hydrologic data assimilation indicate that CopPF can rejuvenate particle evolution in large spaces and thus achieve good performances with low sample size scenarios. The applicability of CopPF is further illustrated through two real‐case studies. It is shown that, compared with traditional particle filter (PF) and particle Markov chain Monte Carlo (PMCMC) approaches, the proposed method can provide more accurate results for both deterministic and probabilistic prediction with a sample size of 100. Furthermore, the sample size would not significantly influence the performance of CopPF. Also, the copula resampling approach dominates parameter evolution in CopPF, with more than 50% of particles sampled by copulas in most sample size scenarios.

Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

This study improved hydrologic data assimilation through integrating the capabilities of particle filter (PF) and ensemble Kalman filter (EnKF) methods, leading to two integrated data assimilation schemes: the coupled EnKF and PF (CEnPF) and parallelized EnKF and PF (PEnPF) approaches. The applicability and usefulness of CEnPF and PEnPF were demonstrated using a conceptual rainfall-runoff model. The performance of two new developed data assimilation methods and traditional EnKF and PF approaches was tested through a synthetic experiment and two real-world cases with one located in the Jing River basin and one located in the Yangtze River basin. The results show that both PEnPF and CEnPF approaches have more opportunities to provide better results for both deterministic and probabilistic predictions than traditional EnKF and PF approaches. Moreover, the computational time of the two integrated methods is manageable. But the proposed PEnPF may need much more time for some large-scale or time-consuming hydrologic models since it generally needs three times of model runs used by EnKF, PF and CEnPF.

Integrated particle smoothing for target tracking in clutter

The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (IPS) algorithm and the IP-Rauch–Tung–Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The IPS algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.

New Particle Filter Based on GA for Equipment Remaining Useful Life Prediction.

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.

Scheduled Health Monitoring of Hybrid Systems With Multiple Distinct Faults

Health monitoring of hybrid systems has attracted substantial attention in recent years. However, two issues exist in the monitored hybrid systems which could add to the difficulty of algorithm development: First, multiple faults with unknown distinct nature may occur simultaneously; and second, fault detection time and mode change time might be negatively correlated which obstructs the completion of fault estimation. To deal with these two problems, a scheduled health monitoring (SHM) method is developed in this paper. First, a unified fault modeling that enables the fault estimation, when the fault nature is unknown, is proposed. After that, to tackle the problem that the fault estimation cannot be completed when the time of fault detection is close to the following mode change time which makes the fault undetectable, a scheduling method is developed where the activation of fault estimator is controlled by the scheduler. For the purpose of fault estimation and prognosis under the SHM framework, a diversity enhanced particle filter (PF) is developed which alleviates the sample impoverishment problem in the traditional PF. Finally, experimental investigations are conducted to validate the proposed methodology.

An Improved Sequential Smoothing Particle Filtering Method

In order to cope with the challenges of non-cooperative targets such as stealth targets to modern radar, especially when traditional threshold detection and tracking methods can hardly detect fast-moving stealth targets, technological innovation has long been required. In this paper we have proposed a new algorithm which can reduce computational cost and improve tracking accuracy. Firstly, the number of particles in the traditional particle filter is reduced and a small number of sampling points are derived from the possible distribution of the target to be tracked, each given a proper weight. Then, the transformed sampling points are sequentially smoothed. And finally, the target positions are estimated. The simulation results show that the proposed algorithm is more accurate than the traditional particle filter algorithm and has lower computational complexity. In the case when SNR is between 0dB to 15dB, a total of 100 Monte Carlo simulations are carried out, obtaining a high detection probability. The detection probability of the improved algorithm is higher than that of the existing particle filter at 7dB. Also, the computational cost is lower than the existing particle filter algorithm.

Multiple Moving Targets Tracking Research in Cluttered Scenes

Traditional particle filter (PF) will have bad effect when the target’s color is similar to the background or the target is blocked, and an improved tracking algorithm is presented in the framework of PF and DSmT.