Resampling Particle Filter Research Articles

Online estimation of inlet contaminant concentration using Eulerian-Lagrangian method of fundamental solutions and Bayesian inference

The advection-diffusion equation is fundamental to modeling various transport phenomena, including the distribution of chemical species in surface or groundwater flow. In cases where the concentration at the source is unknown, inverse problem formulations are required to estimate the desired states by assimilating concentration monitoring data from specific points along the watercourse using a Bayesian approach. This paper proposes a combination of the Eulerian-Lagrangian method and the meshless method of fundamental solutions to solve the advection-diffusion equation. Moreover, the method was used as an evolution model for the sequential importance resampling particle filter algorithm to reconstruct the time-dependent inlet pollutant concentration in inverse problems. Numerical smooth and discontinuous inlet function results show that the particle filter - Eulerian-Lagrangian method of fundamental solutions combination can reconstruct inlet concentration time series.

An improved resampling particle filter algorithm based on digital twin

An improved resampling particle filter algorithm based on digital twin

Retraction Note: Unsupervised video multi-target tracking based on fast resampling particle filter

Retraction Note: Unsupervised video multi-target tracking based on fast resampling particle filter

Physics-Informed Neural Network for monitoring the sulfate ion adsorption process using particle filter.

Fixed-bed columns are a well-established water purification technology. Several models have been constructed over the decades to scale up and predict the breakthrough curve of an adsorption column varying the flow rate, length, and initial concentration of solute. In this work, we proposed using an emerging computational approach of a physic-informed neural network (PINN) that uses artificial intelligence to solve the partial differential equation model of adsorption. The effectiveness of this approach is compared with finite-volume methods and experimental data. We also couple the PINN with a sampling importance resampling particle filter, a Bayesian technique that allows the filter and estimate states of the process, quantifying uncertainties of experimental measurements. The results shows physic-informed neural network capability in solving the proposed model and its uses as an evolution model for sequential estimation.

Vision-based particle filtering for quad-copter attitude estimation using multirate delayed measurements.

In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello's image frames.

On parameter bias in earthquake sequence models using data assimilation

Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and shear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation using a sequential-importance resampling particle filter in a zero-dimensional (0D) generalization of the Burridge–Knopoff spring–block model with rate-and-state friction. Minor changes in the friction parameter ϵ can lead to different state trajectories and earthquake characteristics. The performance of data assimilation with respect to estimating the fault state in the presence of a parameter bias in ϵ depends on the magnitude of the bias. A small parameter bias in ϵ (+3 %) can be compensated for very well using state estimation (R2 = 0.99), whereas an intermediate bias (−14 %) can only be partly compensated for using state estimation (R2 = 0.47). When increasing particle spread by accounting for model error and an additional resampling step, R2 increases to 0.61. However, when there is a large bias (−43 %) in ϵ, only state-parameter estimation can fully account for the parameter bias (R2 = 0.97). Thus, simultaneous state and parameter estimation effectively separates the error contributions from friction and shear stress to correctly estimate the current and future shear stress and slip rate. This illustrates the potential of data assimilation for the estimation of earthquake sequences and provides insight into its application in other nonlinear processes with uncertain parameters.

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

AbstractProxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter‐annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP‐class GCMs. We examine in a perfect‐model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1‐year lead‐time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM‐emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold

• A dependent competing failure model is established for drill lifetime estimation. • The effect of wear degradation on the sudden failure threshold is considered. • Reliability functions and useful life expression of the drill are derived. • A drilling experiment is provided for validating the proposed method. Accurate remaining useful life (RUL) prediction of the drill bit is central to ensuring the processing quality of products and enhancing processing safety and reliability. However, the majority of current methods are confined to considering the wear degradation during the drilling and ignore the combined effect of wear degradation and random shock on RUL. Therefore, it is difficult to the sudden failure prediction of the drill bit. To address this limitation, a degradation-shock dependent competing failure processes (DCFPs) based method considering time-shifting sudden failure threshold is proposed in this paper for RUL prediction. First, a degradation-shock DCFPs model is established based on the processing characteristics of the drill bit. Further, the expressions of reliability function and useful life taking into account the time-shifting sudden failure threshold are derived. Then, based on the Bayesian method, a two-step maximum likelihood estimation (MLE) is designed for parameter estimation, and the crucial parameters are updated utilizing a local gaussian importance resampling particle filter. Finally, a carbon composite laminate drilling experiment is provided for demonstration. Experimental results demonstrate the superiority of the proposed method for drill bit RUL prediction, especially when the drill bit is chipped.

Improved weighting in particle filters applied to precise state estimation in GNSS.

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%.

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.

Multi-prediction metropolis hastings resampling filtering algorithm based on CUDA

Aiming at the problems of accuracy, speed reduction and estimation accuracy loss caused by MH instead of sequential importance resampling in Metropolis Hastings resampling particle filter algorithm , this paper proposes a parallel Metropolis hasting filter algorithm based on a multi-prediction framework, which loads particles Filtering shifts from resampling to prediction and update steps. The overhead of the Multi-prediction framework can be easily compensated by parallel implementation. This algorithm reduces global sequential operations by adding local parallel computing. Simulation experiments prove that the real-time performance and state estimation accuracy of this method have been improved.

Application of an improved particle filter for random seismic noise suppression

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

Remaining useful life prediction for lithium-ion battery by combining an improved particle filter with sliding-window gray model

Dependable and accurate battery remaining useful life (RUL) prediction is essential for ensuring the safety and reliability of battery systems. To improve the dynamic traceability of the battery degradation process for RUL prediction under different loading profiles, this paper presents an improved RUL prediction method, which is established from the combination of the linear optimization resampling particle filter (LORPF) with the sliding-window gray model (SGM). Major innovations are presented as follows: (1) To increase the accuracy of RUL prediction, a linear optimization combination is proposed to overcome the particle diversity deficiency in the resampling process of the standard PF, i.e. the LORPF; (2) To improve the traceability of the LORPF in predicting degradation trajectory, the SGM is employed to update the state variables of the state–space model in the LORPF. Additionally, an SGM-LORPF framework is constructed for RUL prediction. The performance of the SGM-LORPF is synthetically verified by data from two types of batteries under different loading profiles. Prediction test results indicate that the SGM-LORPF can achieve accurate RUL prediction under both constant current discharge conditions (relative error within 7.20%) and dynamic current discharge conditions (relative error within 2.75%). Moreover, using only a small amount of historical data, the proposed SGM-LORPF framework can acquire accurate results. The experimental outcome indicates that the SGM-LORPF has considerable efficiency and a wide range of practicality.

Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection

Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.

A blind separation algorithm for heterogeneous mixed signals

Aiming at the problem that the existing algorithms can only achieve single-channel blind separation (SCBS) for signals with co-frequency and co-modulated, the gravitational field resampling particle filter (GFR-PF) algorithm was proposed. The GFR-PF can realize SCBS of binary phase shift keying (BPSK) and binary frequency shift keying (2FSK) that overlap in both time and frequency domain and are mixed in a single-channel with environmental noise. The GFR-PF was used to jointly detect symbols and estimate unknown parameters of the BPSK and 2FSK signals. Besides, the blind separation of different frequencies and different modulations signals, which was called the mixed heterogeneous signal blind separation (HSBS). The proposed algorithm not only improved the global search performance and tracking accuracy of the blind separation of the same frequency and same modulation mixed signals but also realized the HSBS of BPSK and 2FSK. Thus, the blind separation of heterogeneous mixed signals containing BPSK and 2FSK with different amplitudes and bit rates were achieved by the proposed algorithm. Based on this heterogeneous mixed signal model, it was proposed by this paper that anti-interception capability was improved through mixed transmission of BPSK and 2FSK signals with different amplitudes.

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

A Neural Network-Based Nonlinear Acoustic Echo Canceller

In this letter, we introduce a novel approach for nonlinear acoustic echo cancellation. The proposed approach uses the principle of transfer learning to train a neural network that approximates the nonlinear function responsible for the nonlinear distortions and generalizes this network to different acoustic conditions. The topology of the proposed network is inspired by the conventional adaptive filtering approaches for nonlinear acoustic echo cancellation. The network is trained to model the nonlinear distortions using the conventional error backpropagation algorithm. In deployment, and in order to account for any variation or discrepancy between training and deployment conditions, only a subset of the network's parameters is adapted using the significance-aware elitist resampling particle filter. The proposed approach is evaluated and verified using synthesized nonlinear distortions and real nonlinear distortions recorded by a commercial mobile phone.