Combination Of Particle Filter Research Articles

Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients’ characteristic data, be more relevant to different patients, and help achieve precision medicine.

Cost-Efficient Real-Time Adaptive Location Tracking With Interacting Multiple Transition Model for Implantable Medical Device

One of the main tasks connected with implantable medical devices, such as wireless capsule endoscope (WCE) is location tracking of a capsule moving inside a human body. To improve the localization accuracy in a real situation, a transition model to describe the capsule movement inside the body should be appropriately considered, where the trajectory segments are composed of different movement models. In this paper, in addition to the conventional random way point model, we constructed an interacting multiple model (IMM), which is a combination of two different movements to represent suitable WCE movement. Then, we proposed a combined extended Kalman and particle filter method with respect to the balance between the accuracy and computational cost for realizing precise real-time WCE location tracking. Compared with conventional tracking methods, the proposed tracking location method with the IMM transition model achieved 30% better estimation accuracy and 98% lower computational cost.

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

A Statistical Hypothesis Testing Strategy for Adaptively Blending Particle Filters and Ensemble Kalman Filters for Data Assimilation

Abstract Particle filters avoid parametric estimates for Bayesian posterior densities, which alleviates Gaussian assumptions in nonlinear regimes. These methods, however, are more sensitive to sampling errors than Gaussian-based techniques such as ensemble Kalman filters. A recent study by the authors introduced an iterative strategy for particle filters that match posterior moments—where iterations improve the filter’s ability to draw samples from non-Gaussian posterior densities. The iterations follow from a factorization of particle weights, providing a natural framework for combining particle filters with alternative filters to mitigate the impact of sampling errors. The current study introduces a novel approach to forming an adaptive hybrid data assimilation methodology, exploiting the theoretical strengths of nonparametric and parametric filters. At each data assimilation cycle, the iterative particle filter performs a sequence of updates while the prior sample distribution is non-Gaussian, then an ensemble Kalman filter provides the final adjustment when Gaussian distributions for marginal quantities are detected. The method employs the Shapiro–Wilk test to determine when to make the transition between filter algorithms, which has outstanding power for detecting departures from normality. Experiments using low-dimensional models demonstrate that the approach has a significant value, especially for nonhomogeneous observation networks and unknown model process errors. Moreover, hybrid factors are extended to consider marginals of more than one collocated variables using a test for multivariate normality. Findings from this study motivate the use of the proposed method for geophysical problems characterized by diverse observation networks and various dynamic instabilities, such as numerical weather prediction models. Significance Statement Data assimilation statistically processes observation errors and model forecast errors to provide optimal initial conditions for the forecast, playing a critical role in numerical weather forecasting. The ensemble Kalman filter, which has been widely adopted and developed in many operational centers, assumes Gaussianity of the prior distribution and solves a linear system of equations, leading to bias in strong nonlinear regimes. On the other hand, particle filters avoid many of those assumptions but are sensitive to sampling errors and are computationally expensive. We propose an adaptive hybrid strategy that combines their advantages and minimizes the disadvantages of the two methods. The hybrid particle filter–ensemble Kalman filter is achieved with the Shapiro–Wilk test to detect the Gaussianity of the ensemble members and determine the timing of the transition between these filter updates. Demonstrations in this study show that the proposed method is advantageous when observations are heterogeneous and when the model has an unknown bias. Furthermore, by extending the statistical hypothesis test to the test for multivariate normality, we consider marginals of more than one collocated variable. These results encourage further testing for real geophysical problems characterized by various dynamic instabilities, such as real numerical weather prediction models.

Improving the estimation of snow depth in the Noah-MP model by combining particle filter and Bayesian model averaging

Noah with multiparameterization (Noah-MP) is a new-generation land surface model that is enhanced by Noah and has multiparameterization as a major feature. Although various schemes have improved the applicability of this model, the ability to reduce the uncertainties of the simulation results remains a challenge. To improve the accuracy of snow-depth prediction based on the multiparameterization schemes within the Noah-MP model, we proposed a novel snow data assimilation framework configured using Bayesian model averaging (BMA) and genetic particle filter (GPF). Within this framework, we employed a kernel density smoother to create a probability density function of the model ensembles that are created using the GPF. We selected representative parameterization scheme combinations within the Noah-MP model to conduct the simulation experiments. We examined the feasibility of BMA applied in snow-depth prediction. On this basis, we investigated the performance of the BMA-GPF at eight seasonally snow-covered study sites with different snow climates. The results showed that BMA can be used to obtain a deterministic snow-depth prediction with a determination coefficient of 93.51% and a containing ration of prediction interval of 94.68%. These results were significantly higher than the four scheme combinations within the training period; however, the bias of deterministic prediction was well below expectations. The containing ration of uncertainty prediction interval was just 70% and was even lower than the four scheme combinations within the test period. The BMA-GPF snow data assimilation framework helped to obtain snow-depth prediction with a containing ration that ranged from 95% to 98% at eight sites and could be used in different snow climates. Further comparative results indicated that the BMA-GPF snow data assimilation framework no longer depended on the correction coefficients of the model prediction, and the assumption that the posterior probability of the state variable obeys the Gaussian distribution was not required. Finally, we concluded that the BMA-GPF is a suitable candidate approach to snow-depth multimodel ensemble simulation.

Hybrid Particle Filter–Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems

This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF–PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.

Research on SLAM Road Sign Observation Based on Particle Filter.

With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality.

Solar Array Power Prediction for Near-Space Vehicles Based on a Hybrid Method

Compared with other energy sources in near-space, solar energy is more renewable, stable and accessible, which makes solar cells the main source of energy for long-endurance near-space vehicles. However, due to the limited solar cells laying area and complex flight conditions, near-space vehicles are facing tense energy supply situation during flight. Therefore, fast and accurately online power prediction of the solar array, which has important significance for power management, efficient use and reliable operation of the energy system, is the key point to solving the situation at present. In this paper, a hybrid online power prediction method combined particle filter and power calculation model is proposed. Firstly, a comprehensive solar cell array power calculation model for the near-space vehicle is established. Then, the particle filter algorithm is used to estimate optimally the important parameters in the power calculation model with the flight data, to realize the dynamic, rapid and accurate prediction of the power generation of the vehicle during flight. Finally, the data obtained from flight tests of the stratospheric aerostat and high-altitude scientific balloon are verified and compared with the other four models. The results demonstrate that the generation power prediction method proposed in this paper has higher prediction accuracy, which will be a good guiding significance for the actual flight of different types of near-space vehicles.

Recycling of Waste SCR Catalysts Using a Catalytic Filter: A Study on the Catalytic Performance for NOx Abatement

In this work, a multifunctional filter with the abilities of particle filtration and NOx removal was fabricated using a combined particle filter for retreating waste catalysts through an impregnati...

A combination state of charge estimation method for ternary polymer lithium battery considering temperature influence

Under the actual operating conditions of electric vehicle, the temperature of the battery system changes with the ambient temperature, its influence is rarely considered on state of charge (SOC) estimation. Aiming at this problem, this work explores the relationship between temperature and battery discharge capacity, capacity loss, coulomb efficiency and open circuit voltage, and establishes a temperature correction model. Then, a temperature-based coulomb counting (TBCC) method is proposed. To eliminate the cumulative errors of SOC estimation, a TBCC combined adaptive particle filter (APF) combination estimation method is proposed, which can update the temperature-related variables, and estimates the system state with the identified model parameters. The estimation method adapts to the temperature change of the power lithium battery while taking into account the superiority of the APF in non-linear and non-Gaussian systems, so as to improve the accuracy of the SOC estimation in the changing temperature range. Simulations under urban dynamometer driving schedule (UDDS) are conducted, and the results show that compared with the method without temperature correction, the proposed combination method can improve the accuracy and convergence ability of SOC estimation under the influence of temperature.

An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm

Multiple vehicle detection is a promising and challenging role in intelligent transportation systems and computer vision applications. Most existing methods detect vehicles with bounding box representation and fail to offer the location of vehicles. However, the location information is vigorous for several real-time applications such as the motion estimation and trajectory of vehicles moving on the road. In this paper, we propose an advanced deep learning method called enhanced you only look once v3 and improved visual background extractor algorithms are used to detect the multi-type and multiple vehicles in an input video. More precisely, tracking is to find the trace of the upcoming vehicles using a combined Kalman filtering algorithm and particle filter techniques. To improve the tracking results, further, we propose the technique, namely multiple vehicle tracking algorithms, and tested with different weather conditions such as sunny, rainy, night and fog in input videos of 30 frames per second. The major research issues were found in the recent kinds of literature in ITS sector which is closely related to the real-time traffic environmental problems such as occlusions, camera oscillations, background changes, sensors, cluttering, camouflage, varying illumination changes in a day- and sunny and at nighttime vision. The experimental results are tested with the ten different input videos and two benchmark datasets KITTI and DETRAC. The most eight high- level features have been considered for automatic feature extraction and annotation. The attributes are length, width, height, number of mirrors and wheels and windscreen shielding glass to detect the target region of interest (vehicles) on road. In addition, further experiments are carried out in multiple-input videos of high definition quality using a monocular camera, and the average accuracy is 98.6%, and the time complexity of the algorithm is O(n) and also tracking results attained 96.6%. The dataset and input videos are discussed in comparative results with the F-test measure done for multiple vehicles.

Combining particle filter algorithm with bio-inspired anemotaxis behavior: A smoke plume tracking method and its robotic experiment validation

The smoke source localization robot is a safe substitution of human and animal rescuers in many dangerous search and rescue mission. In this paper, we propose an anemotaxis – particle filter based smoke plume path tracking method and presented a smoke source localization robot. A modified firefly algorithm is applied in the resampling step of particle filter based smoke plume tracking method to mimic anemotaxis behaviors in the odor source searching process of living creatures. The performance of the proposed algorithm is qualitatively evaluated in a simulated wind tunnel environment in Webots, a high-fidelity robotic simulator. The average computation time including plume path estimation, resampling and target evaluation in every step is 35.9974 ms and the average smoke source localization error is 0.7769 m. We also verify the feasibility by running the proposed algorithm on the smoke source localization robot in real experiments.

Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting

To effectively leverage the spatio-temporal sensing capabilities of the team searching for a signal-emitting source, this paper presents a collaborative search method, in which each robot employs the weighted social Bayesian estimation and executes the distributed infotaxis search for the source. Cognition difference between robots, measuring the dissimilarity of probability maps, is specially introduced to obtain the heterogeneous weights of Bayesian estimation. However, the requirement of exchanging the whole probability map presents additional challenges in computation and communication for real-time applications. In this work, a solution for fast low-cost collaborative infotaxis method based on a combination of particle filter and Gaussian fitting is proposed. A particle filter is first employed for the representation of the source probability distribution, which makes the infotaxis strategy computationally tractable for large complex spaces using the limited and tractable amount of randomly drawn particles. By fitting a Gaussian density to the particles, each robot obtains the likelihood weight for social Bayesian estimation by only reporting the mean and the covariance matrix of Gaussian distribution rather than exchanging the whole probability maps. The simulation shows the proposed collaborative infotaxis can achieve an efficient search behavior in complex environments using a small number of particles and a lower communication bandwidth.

MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

Gesture Tracking and Locating Algorithm Based on Federated Tracking Filter

Abstract In this paper, a joint gesture tracking method combining particle filter and mean shift algorithm is proposed to improve the accuracy and robustness of the system. During the slow movement of the human hand, the average movement of the particles is first used to cause most of the particles to drift into the gesture area. In the case where the movement of the human hand is faster or there is occlusion, when the mean shift of the particle is performed, if the region of the gesture is not detected, the particle will return to the state before the drift, and then the next frame is processed. The method can directly calculate the position of the gesture based on the particles used for subsequent testing, and can save the tracking time of the algorithm. Through experimental simulation, compared with the Cam-shift algorithm, when the sampling point of the joint tracking algorithm proposed in this paper is 200, the tracking accuracy is improved to 95.2%. Compared with 90.6% of the Cam-shift algorithm, the tracking time is reduced from 83.7 ms to 25.8 ms. Therefore, the proposed algorithm can greatly improve the tracking accuracy and real-time, and can also effectively reduce the impact of complex environments on the tracking effect.

Multiple pedestrian tracking by combining particle filter and network flow model

A novel network flow model is proposed for multiple pedestrian tracking in this paper. Based on tracklets, only a short and reliable detection sequence is needed for effective tracking. Our model fuses the local and global data association strategies to compensate for their respective shortcomings, and can be divided into two stages: a local stage and a global stage. In the local stage, we follow the tracking-by-detection framework to generate confident tracklets by employing a boosted particle filter. In the global stage, the data association is formulated as a Maximum-a-Posteriori (MAP) problem and solved by a typical min-cost flow algorithm. A double-step optimization is designed to solve the long term occlusion. The experimental results show that our method outperforms several state-of-the-art methods for multiple pedestrian tracking.

Robust multi‐view videos face recognition based on particle filter with immune genetic algorithm

The task of face recognition in real-world scenarios is still a challenging one. There exist many techniques for the recognition of faces in videos. The recognition task may be computationally easier, but is susceptible to pose variations, lighting conditions etc. This paper focuses on recognition of faces from multi-view videos using the combination of particle filtering with Immune Genetic Algorithm (IGA) and HSH, which is insensitive to pose variations. Particle filtering along with the IGA efficiently track the target using immune system mechanism and then the recognition phases are carried out using HSH. For recognition of video, the ensemble feature similarity calculated which can be measured with the limiting Bhattacharyya distance of features in the Reproducing Kernel Hilbert space. The proposed system with HSH provides better performance than using Spherical Harmonics (SH) for recognising the face of the target and the performance are analysed with existing techniques for recognition of face.

A hybrid prognostic method for system degradation based on particle filter and relevance vector machine

Prognostics of the remaining useful life has become a critical technique to ensure the reliability and safety of system, however, due to the uncertainty of system degradation, the prognostic result is usually not so satisfactory. To solve this problem, a hybrid prognostic scheme with the capability of uncertainty assessment is proposed in this paper, which combines particle filter (PF) and relevance vector machine (RVM). The prognostic result comprises a set of deterministic prediction values to represent the degradation process and a prediction interval to evaluate the prediction uncertainty. In order to examine the performance of the proposed hybrid method, four types of comparative experiments based on two types of lithium-ion battery datasets and two degradation models are performed. The experimental results show that the proposed hybrid scheme is a reliable prognostic method which can ensure the accuracy of the deterministic prediction result and provide precise assessment for the prediction uncertainty.

Large-Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.

Adaptive Framework for Robust Visual Tracking

Visual tracking is a difficult and challenging problem, for numerous reasons such as small object size, pose angle variations, occlusion, and camera motion. Object tracking has many real-world applications such as surveillance systems, moving organs in medical imaging, and robotics. Traditional tracking methods lack a recovery mechanism that can be used in situations when the tracked objects drift away from ground truth. In this paper, we propose a novel framework for tracking moving objects based on a composite framework and a reporter mechanism. The composite framework tracks moving objects using different trackers and produces pairs of forward/backward tracklets. A robustness score is then calculated for each tracker using its forward/backward tracklet pair to find the most reliable moving object trajectory. The reporter serves as the recovery mechanism to correct the moving object trajectory when the robustness score is very low, mainly using a combination of particle filter and template matching. The proposed framework can handle partial and heavy occlusions; moreover, the structure of the framework enables integration of other user-specific trackers. Extensive experiments on recent benchmarks show that the proposed framework outperforms other current state-of-the-art trackers due to its powerful trajectory analysis and recovery mechanism; the framework improved the area under the curve from 68% to 70.8% on OTB-100 benchmark.