Standard Particle Filter Research Articles

Slam algorithm for multi-robot communication in unknown environment based on particle filter

To solve the SLAM (simultaneous localization and map building) of mobile robots, a multi-robot SLAM algorithm based on particle filter for communication in unknown environment is proposed. In the standard particle filter, the incremental map construction method based on point-line consistency is introduced to preserve the hypothesis of the line segment feature map in each particle, and the importance function is introduced to the observation information. On this basis, sampling map fusion algorithm extracts ORB (oriented FAST and rotated BRIEF) features of local map constructed by a single robot and finds the optimal matching points, calculates the homography matrix of local map optimal matching point set, determines the optimal affine transformation parameters among point sets, so as to complete the local map fusion, and realize the multi-robot communication based on SLAM in unknown environment. The results show that the algorithm achieves high-precision map fusion and it is an effective SLAM construction algorithm for multiple machines.

Estimation for heterogeneous traffic using enhanced particle filters

This article explores the state estimation problem for heterogeneous traffic (vehicles with distinct driving behaviours) using particle filtering (PF) approaches. We consider three variations of PF to enhance estimation. The benchmark PF utilises a deterministic partial differential equation and a state-independent additive process noise. We first consider a parameter-adaptive PF variation that also allows model parameters to be adjusted. The second variation is a PF with spatially-correlated noise. The last variation combines parameter-adaptive and the spatially-correlated-noise approaches. We compare the four filters in numerical experiments that represent heterogeneous traffic scenarios and on real-world heterogeneous traffic data. The results show that the enhanced filters can achieve up to an 80% and 46% of accuracy improvement as compared to an open loop simulation without measurement correction, with the synthetic settings and with real traffic data, respectively. Moreover, the enhanced filters outperform the standard PF in all the traffic scenarios based on accuracy.

Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter

The prognosis of time‐to‐failure for a battery can avoid the failure caused by battery performance loss. In this paper, a novel and effective algorithm is proposed to predict the remaining useful life of lithium‐ion batteries. The extended Kalman particle filter is used to improve particle degradation problem existing in standard particle filter algorithm. In order to fit battery capacity degradation, a transformed model is proposed based on double exponential empirical degradation model. It can reduce the number of parameters and the training difficulty of parameters; it also matches the form of state transfer equation. In order to improve prediction accuracy, the auto regression model is introduced to correct observation values produced by observation equation. Experimental results show that the proposed algorithm can effectively improve the accuracy of prediction compared with other algorithms. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Piecewise-linear approximations and filtering for DSGE models with occasionally-binding constraints

We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally-binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound.

Particle Filters: A Hands-On Tutorial.

The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and code examples. Extensive research has advanced the standard particle filter algorithm to improve its performance and applicability in various ways in the years after. As a result, selecting and implementing an advanced version of the particle filter that goes beyond the standard algorithm and fits a specific estimation problem requires either a thorough understanding or reviewing large amounts of the literature. The latter can be heavily time consuming especially for those with limited hands-on experience. Lack of implementation details in theory-oriented papers complicates this task even further. The goal of this tutorial is facilitating the reader to familiarize themselves with the key concepts of advanced particle filter algorithms and to select and implement the right particle filter for the estimation problem at hand. It acts as a single entry point that provides a theoretical overview of the filter, its assumptions and solutions for various challenges encountered when applying particle filters. Besides that, it includes a running example that demonstrates and implements many of the challenges and solutions.

Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization

The standard particle filter (PF) algorithm has the issue of particle diversity loss caused by particle degradation and resampling, which makes it impossible for particle samples to accurately represent the true distribution of state probability density function. Particle swarm optimization (PSO) algorithm can effectively improve the particle degradation problem of particle filter namely, PSO-PF, but its fitness function is greatly affected by the variance of measurement noise, and is easy to fall into local optimal, which greatly limits the filtering accuracy. Therefore, this paper proposes an algorithm that combines genetic algorithm (GA) and PSO algorithm to improve particle filtering, namely, GA-PSO-PF. This algorithm combines the fast convergence speed of particle swarm optimization with the strong global searching ability of genetic algorithm to increase the diversity of particles while ensuring the effectiveness of superior particles, and improve the speed and accuracy of finding the optimal solution. Experimental results show that the filtering performance of the proposed algorithm is better than PF and PSO-PF, and the positioning and tracking accuracy is improved by 54.44% compared with PF and 27.20% compared with PSO-PF.

Guided navigation from multiple viewpoints using qualitative spatial reasoning

ABSTRACT Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localization and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location. In order to accomplish this, the present paper assumes relations from the qualitative spatial reasoning formalism called StarVars, whose inference method is also used to build a model of the domain. This paper presents two qualitative-probabilistic algorithms for guided navigation using a particle filter and qualitative spatial relations. In the first algorithm, the particle filter is run upon a qualitative representation of the domain, whereas the second algorithm transforms the numerical output of a standard particle filter into qualitative relations to guide a sensory deprived robot. The proposed methods were evaluated with experiments carried out on a 2D humanoid robot simulator. A proof of concept executing the algorithms on a group of real humanoid robots is also presented. The results obtained demonstrate the success of the guided navigation models proposed in this work.

Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system

It is technically difficult to accurately establish a dynamic adaptive model of an aluminium electrolysis manufacturing system (AEMS) because the system has many complex characteristics, such as multiple parameters, dynamic time variance, and a non-Gaussian distribution of process data. Inspired by the overall superiority of particle filtering theory in dealing with non-linear non-Gaussian problems, this paper presents a novel method based on a multi-sampling inherited hybrid annealed particle filter neural network (MSI-HAPFNN). Firstly, the neural network’s (NN’s) weights and thresholds are used as the state variables of hybrid annealed particle filter; Secondly, the hybrid proposal distribution obtained by sampling the above state variables is employed to replace the posterior proposal distribution in the standard particle filter (PF) algorithm as the importance density function, thereby adjusting the NN’s weights and thresholds in real time. Thirdly, the model achieves the features of multi-sampling and inheritance by introducing NN and PF weights, and using adaptive inheritance method. Therefore, this paper systematically proposes the theoretical construction framework and experimental procedure of MSI-HAPFNN. Furthermore, this article also introduces a genetic algorithm to thoroughly evaluate the prediction potential. The proposed model has been tested on the real-world system for aluminium electrolysis manufacturing and compared with several closely related frameworks. The experimental results show that the MSI-HAPFNN model can significantly improve the self-adaptive ability of the object system to working conditions and the prediction accuracy of power consumption, which is helpful in finding optimal design parameters in an AEMS.

An Optimized Coverage Robot SLAM Algorithm Based on Improved Particle Filter for WSN Nodes

In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot SLAM algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter SLAM algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the SLAM positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot SLAM information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot SLAM is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.

Artificial evolution based cost-reference particle filter for nonlinear state and parameter estimation in process systems with unknown noise statistics and model parameters

The cost-reference particle filter (CRPF) is a variant of the particle filter (PF). It is simpler, more robust, and more flexible than the standard PF. Particularly, it does not require any statistical information on both state noises and observation noises in its application. However, in order to successfully apply CRPF to the nonlinear state estimation in dynamic process systems, the knowledge of the model parameters should be known a priori. The standard CRPF cannot handle the problem of state and parameter estimation (SPE) with unknown noise statistics and model parameters. To eliminate the above limitation, this paper proposes an evolution algorithm for the SPE in nonlinear dynamic process systems. The algorithm is the combination of the artificial evolution (AE) with CRPF, called AE-CRPF, to estimate the states and the parameters simultaneously. The proposed AE-CRPF is applied to two nonlinear dynamic process systems for practical applications. The results demonstrate the effectiveness and robustness of the proposed method.

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

Tracking by Risky Particle Filtering over Sensor Networks.

The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.

Intelligent adaptive unscented particle filter with application in target tracking

The particle filter (PF) perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. However, the standard PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, intelligent adaptive unscented particle filter (IAUPF) is proposed in this paper. The IAUPF uses an adaptive unscented Kalman filter filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators to increase diversity of particles. Three experiment examples show that IAUPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. The effectiveness of IAUPF is demonstrated through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method.

Parameter estimation based-FDI method enhancement with mixed particle filter

Mobile robots have a crucial role in many domains. Currently, service robots can be found in our homes, transportation robots in warehouses, logistic robots in factories and even on Mars, where rovers are exploring its surface. These intelligent machines are becoming increasingly autonomous, as they have to achieve longer and longer missions. Thus, to maintain autonomy and to guarantee material security, Fault Detection and Isolation (FDI) is a mandatory task. A fault causes a mission to fail or, worse, it may damage materials and operators if the robot goes out of control. In this paper, we propose to address the FDI process. First, we present two modified versions of two known methods based on parameter estimation. Then, a mixed version of Particle Filter (PF) which combines a standard PF and a Gaussian PF to generate a better performing FDI method is proposed. These methods are studied in the case of a wheel block fault. Simulation and real robot experimentation results prove the added value of our proposed FDI method over the other two approaches.

A Zero-Velocity Update Method for Adaptive Particle Filtering

Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.

Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints

We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound.

Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method

For battery management system, accurate estimation of state of charge (SOC) and state of health (SOH), as well as prediction of remaining useful life (RUL) are of great significance. Herein, backward smoothing square root cubature Kalman filter (BS-SRCKF) is proposed to improve accuracy and convergence speed of SOC estimation. Then the multiscale hybrid Kalman filter (MHKF), which consists of BS-SRCKF and extended Kalman filter (EKF), is employed for the joint estimation of SOC and SOH. Furthermore, improved cuckoo search (ICS) algorithm is embedded in the standard particle filter (PF) to improve its performance, by transferring the particles in the prior distribution region to the maximum likelihood region. Eventually, RUL prediction is achieved based on the SOH information estimated by the joint estimation of SOC and SOH and the improved cuckoo search particle filter (ICS-PF). The simulation results demonstrate that the method for RUL prediction present in this paper has improved accuracy, confidence level and resampling rate compared with two existing methods based on PF and unscented particle filter (UPF).

A Defensive Marginal Particle Filtering Method for Data Assimilation

Particle filtering (PF) is an often used method to estimate the states of dynamical systems. A major limitation of the standard PF method is that the dimensionality of the state space increases as the time proceeds and eventually may cause degeneracy of the algorithm. A possible approach to alleviate the degeneracy issue is to compute the marginal posterior distribution at each time step, which leads to the so-called marginal PF method. A key issue in the marginal PF method is to construct a good sampling distribution in the marginal space. When the posterior distribution is close to Gaussian, the ensemble Kalman filter (EnKF) method can usually provide a good sampling distribution; however the EnKF approximation may fail completely when the posterior is strongly non-Gaussian. In this work we propose for modest dimensional filtering problems a defensive marginal PF algorithm which constructs a sampling distribution in the marginal space by combining the standard PF and the EnKF approximation using a multiple importance sampling (MIS) scheme. An important feature of the proposed algorithm is that it can automatically adjust the relative weight of the PF and the EnKF components in the MIS scheme in each step, according to how non-Gaussian the posterior is. With numerical examples we demonstrate that the proposed method can perform well regardless of whether the posteriors can be well approximated by Gaussian.

Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints

We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces the persistence of the Markov chain, improves the accuracy of Monte Carlo approximations of posterior moments, and drastically speeds up computations. We use the techniques to estimate a small-scale DSGE model to assess the effects of the government spending portion of the American Recovery and Reinvestment Act in 2009 when interest rates reached the zero lower bound.