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Academic literature on the topic 'Adaptive Particle Filter'

Author: Grafiati

Published: 3 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Adaptive Particle Filter"

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Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 4 (2005): 1053–68. http://dx.doi.org/10.1239/jap/1134587816.

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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

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Papavasiliou, Anastasia. "A uniformly convergent adaptive particle filter." Journal of Applied Probability 42, no. 04 (2005): 1053–68. http://dx.doi.org/10.1017/s0021900200001108.

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Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

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Guo, Yuyang, Xiangbo Xu, and Miaoxin Ji. "A Zero-Velocity Update Method for Adaptive Particle Filtering." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (2020): 427–33. http://dx.doi.org/10.1051/jnwpu/20203820427.

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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.

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Xue, Li, Shesheng Gao, and Yongmin Zhong. "Robust Adaptive Unscented Particle Filter." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (2013): 55–66. http://dx.doi.org/10.4018/ijimr.2013040104.

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This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.

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Pei, Fujun, Mei Wu, and Simin Zhang. "Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/239531.

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The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness.

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Xue, Li, Chunning Na, and Yulan Han. "Improved Auxiliary Particle Filter for SINS/SAR Navigation." Mathematical Problems in Engineering 2021 (January 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/6635390.

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In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.

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Zuo, J. ‐Y, Y. ‐N Jia, Y. ‐Z Zhang, and W. Lian. "Adaptive iterated particle filter." Electronics Letters 49, no. 12 (2013): 742–44. http://dx.doi.org/10.1049/el.2012.4506.

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Zhou, Junchuan, Stefan Knedlik, and Otmar Loffeld. "INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter." Journal of Navigation 63, no. 3 (2010): 491–511. http://dx.doi.org/10.1017/s0373463310000068.

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With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

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Yu, Wen Tao, Jun Peng, and Xiao Yong Zhang. "A New Adaptive UPF Algorithm through Improved Relative Entropy." Advanced Materials Research 658 (January 2013): 569–73. http://dx.doi.org/10.4028/www.scientific.net/amr.658.569.

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Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

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Stordal, Andreas S., and Hans A. Karlsen. "Large Sample Properties of the Adaptive Gaussian Mixture Filter." Monthly Weather Review 145, no. 7 (2017): 2533–53. http://dx.doi.org/10.1175/mwr-d-15-0372.1.

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In high-dimensional dynamic systems, standard Monte Carlo techniques that asymptotically reproduce the posterior distribution are computationally too expensive. Alternative sampling strategies are usually applied and among these the ensemble Kalman filter (EnKF) is perhaps the most popular. However, the EnKF suffers from severe bias if the model under consideration is far from linear. Another class of sequential Monte Carlo methods is kernel-based Gaussian mixture filters, which reduce the bias but maintain the robustness of the EnKF. Although many hybrid methods have been introduced in recent years, not many have been analyzed theoretically. Here it is shown that the recently proposed adaptive Gaussian mixture filter can be formulated in a rigorous Bayesian framework and that the algorithm can be generalized to a broader class of interpolated kernel filters. Two parameters—the bandwidth of the kernel and a weight interpolation factor—determine the filter performance. The new formulation of the filter includes particle filters, EnKF, and kernel-based Gaussian mixture filters as special cases. Techniques from particle filter literature are used to calculate the asymptotic bias of the filter as a function of the parameters and to derive a central limit theorem. The asymptotic theory is then used to determine the parameters as a function of the sample size in a robust way such that the error norm vanishes asymptotically, whereas the normalized error is sample independent and bounded. The parameter choice is tested on the Lorenz 63 model, where it is shown that the error is smaller or equal to the EnKF and the optimal particle filter for a varying sample size.

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Dissertations / Theses on the topic "Adaptive Particle Filter"

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Krengel, Annette [Verfasser]. "A Modified Particle Filter with Adaptive Stepsize for Continuous-Time Models with Measurement Time Uncertainties / Annette Krengel." München : Verlag Dr. Hut, 2013. http://d-nb.info/1031845100/34.

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Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.

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Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.<br>Ph. D.

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Zhao, Yubin [Verfasser]. "Adaptive Particle Filters for Wireless Indoor Target Tracking / Yubin Zhao." Berlin : Freie Universität Berlin, 2014. http://d-nb.info/1062950186/34.

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Xie, Bei. "Partial Update Adaptive Filtering." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26670.

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Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. An analysis for different PU adaptive filter algorithms is necessary and meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. It is related to the partial update adaptive filter, but has different performance. It can be viewed as a PU adaptive filter, in that the deficient-length adaptive filter also updates part of the weight vector. However, it updates the same part of the weight vector for each iteration, while the partial update adaptive filter updates a different part of the weight vector for each iteration. In this work, basic PU methods are applied to the adaptive filter algorithms which have not been fully addressed in the literature, including CG, EDS, and Constant Modulus Algorithm (CMA) based algorithms. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical analysis is shown including convergence condition, steady-state performance, and tracking performance. Computer simulation with proper examples is also shown to further help study the performance. The performance is compared among different PU methods or among different adaptive filtering algorithms. Computational complexity is calculated for each PU method and each adaptive filter algorithm. The deficient-length RLS and EDS are also analyzed and compared to the performance of the PU adaptive filter. In this dissertation, basic partial-update methods are applied to adaptive filter algorithms including CMA1-2, NCMA, Least Squares CMA (LSCMA), EDS, and CG. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. Deficient-length RLS and EDS are studied. The performance of the deficient-length filter is also compared with the partial update filter. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application is using PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application is using PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification. Overall, the PU adaptive filters can usually achieve comparable performance to the full-update filters while reducing the computational complexity significantly. The PU adaptive filters can achieve similar steady-state MSE to the full-update filters. Among different PU methods, the MMax method has a convergence rate very close to the full-update method. The sequential and stochastic methods converge slower than the MMax method. However, the MMax method does not always perform well with the LSCMA algorithm. The sequential LSCMA has the best performance among the PU LSCMA algorithms. The PU CMA may perform better than the full-update CMA in tracking a time-varying system. The MMax EDS can converge faster than the MMax RLS and CG. It can converge to the same steady-state MSE as the MMax RLS and CG, while having a lower computational complexity. The PU LMS and PU EDS can also perform a little better in a system combining hyperspectral image compression and classification.<br>Ph. D.

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Leitao, Joao. "Continuous states conditional random fields training using adaptive integration." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/3095.

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The extension of Conditional Random Fields (CRF) from discrete states to continuous states will help remove the limitation of the number of states and allow new applications for CRF. In this work, our attempts to obtain a correct procedure to train continuous state conditional random fields through maximum likelihood are presented. By deducing the equations governing the extension of the CRF to continuous states it was possible to merge with the Particle Filter (PF) concept to obtain a formulation governing the training of continuous states CRFs by using particle filters. The results obtained indicated that this process is unsuitable because of the low convergence of the PF integration rate in the needed integrations replacing the summation in CRFs. So a change in concept to an adaptive integration scheme was made. Based on an extension of the Binary Space Partition (BSP) algorithm an adaptive integration process was devised with the aim of producing a more precise integration while retaining a less costly function evaluation than PF. This allowed us to train continuous states conditional random fields with some success. To verify the possibility of increasing the dimension of the states as a vector of continuous states a scalable version was also used to briefly assess its fitness in two-dimensions with quadtrees. This is an asymmetric two-dimensional space partition scheme. In order to increase the knowledge of the problem it would be interesting to have further information of the relevant features. A feature selection embedded method was used based on the lasso regulariser with the intention of pinpointing the most relevant feature functions indicating the relevant features.

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Liu, Shuyue S. M. Massachusetts Institute of Technology. "Adapting Consumer Report's product evaluation methods for particle removal, gravity non-electric and reverse osmosis water filters in the Indian marketplace." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97798.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 113-115).<br>Household Water Treatment and Storage (HWTS) products provides households that are drinking unimproved water supplies with a first line of defense against contaminants in their drinking water and those drinking improved water supplies with an additional barrier against potential risks. With the global water crisis becoming more and more severe, evaluation of HWTS technologies and products has become crucial to ensure they are used to remove impurities effectively. The goal of this thesis was to evaluate household water filters in the Indian marketplace as part of a larger research and technology evaluation to investigate the utilization and performances of different water filter models in both lab and field settings. This was achieved by comparative testing and research work done at Consumer Reports (CR) Headquarters in Yonkers, NY. This evaluation included the testing of three categories of filters: Conventional Particle Removal (cloth and mesh), Gravity Non-Electric (GNE) and Reverse Osmosis (RO) water filters. In total, 16 models were tested. The challenge water for all filter testing had these characteristics: 40+/-10 NTU turbidity and 1500+/-150mg/L total dissolved solids (TDS). When testing E.coli removal, deionized water was used as the base water and the concentration of E. coli was 10⁵ to 10⁶ MPN (Most Probable Number)/100mL. The comparative testing attributes that were evaluated include: E.coli removal, turbidity removal, TDS removal, clean water flow rate, RO % recovery, and filter lifetime with the end-of-life defined as when flow rate <1 L/hr. As a result of this product evaluation, the author determined that: 1) Cloth and mesh filters had limited effectiveness in reducing contaminants; 2) GNE filters had much better performance than cloth and mesh filters, but none of them had outstanding performance; 3) RO filters were shown to be quite effective in reducing turbidity (greater than 99.5%), TDS (greater than 97%), and E.coli (greater than 99.9999%). But, they produce a large amount of wastewater (around 3/4 of the feed water) which is a huge waste of precious water and a sustainability concern especially in a water scarce region.<br>by Shuyue Liu.<br>S.M.

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Zheng, Wenlong. "Face detection and tracking using a boosted adaptive particle filter." 2005. http://purl.galileo.usg.edu/uga%5Fetd/zheng%5Fwenlong%5F200512%5Fms.

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Thesis (M.S.)--University of Georgia, 2005.<br>Directed by Suchendra M. Bhandarkar. Includes an article submitted to IEEE transactions on pattern analysis and machine intelligence. Includes bibliographical references (leaves 98-108).

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Lin, Yi-lung, and 林義龍. "A adaptive Particle Filter based method for Real Time Face Tracking." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/87567895320991714002.

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碩士<br>國立宜蘭大學<br>資訊工程研究所碩士班<br>100<br>The video surveillance systems of recent years, usually, it is major focus at the Human-Face of observation and detection -- Human-Face is the most characteristic and prominent feature of a human, therefore, detection and tracking of Human-Face has become an important indicator of the study. This paper discusses video surveillance of public places, it is major automated face detection and face tracking，The main detection methods is the use of Haar-Like Feature-based and through the Cascade classifier of the Adaboost face detection，In the tracking mechanism is based on particle filter，Last modified SURF (Speeded Up Robust Features) auxiliary particle filter tracking, and thus enhance the detection and tracking accuracy. Keywords：Particle Filter，Adaboost，Haar-Like Feature，SURF。

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Lin, Kun-Chieh, and 林琨傑. "Adaptive Unscented Particle Filter approach for GPS/INS navigation system design." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/45253470155605080328.

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碩士<br>國立臺灣海洋大學<br>通訊與導航工程學系<br>99<br>The paper present the application of fuzzy unscented particle filter(fuzzy UPF) to GPS/INS tight integration and ultra-tight integration design of the navigation system. Ultra-tight is also known as deep integration, it can make receiver have better wide of tracking bandwidth and suppression noise, promote GPS receiver performance, when the GPS is no signal, INS in the receiver's acquisition and re-acquisition process, you can still use the position, velocity on the code loop and carrier loop initialization and parameter external estimation assisted, thus promote receiver tracking loop performance. The Doppler compensation has more errors in high dynamic, I use the velocity aiding to reduce error and promote the accuracy. The particles filter (PF) exhibits superior performance as compared to EKF and UKF instate estimation for the nonlinear, non-Gaussian system. Nevertheless, the degeneracy of particles and accumulation of estimation errors in the PF are difficult to overcome. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the unscented particle filter (UPF). This paper presents a sensor fusion method based on the combination of adaptive unscented particle filter (UPF) and Fuzzy Logic Adaptive System (FLAS) for ultra-tightly coupled GPS/INS integrated navigation. Performance assessment for EKF、UKF、UPF and Fuzzy UPF carried out. The Fuzzy UPF algorithm demonstrates remarkable improvement in navigation estimation accuracy and to reduce the number of particles as compared to the conventional approaches.

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"Adaptive Operation Decisions for a System of Smart Buildings." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.15032.

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abstract: Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.<br>Dissertation/Thesis<br>Ph.D. Industrial Engineering 2012

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Books on the topic "Adaptive Particle Filter"

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Weickert, Joachim. Anisotropic diffusion in image processing. B.G. Teubner, 1998.

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Book chapters on the topic "Adaptive Particle Filter"

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Li, Peng, and Jian Tang. "Adaptive Particle Filter with Estimation Windows." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_8.

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de Campos Velho, H. F., and H. C. Morais Furtado. "Adaptive Particle Filter for Stable Distribution." In Integral Methods in Science and Engineering. Birkhäuser Boston, 2011. http://dx.doi.org/10.1007/978-0-8176-8238-5_6.

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Lang, Hao, Tiancheng Li, Gabriel Villarrubia, Shudong Sun, and Javier Bajo. "An Adaptive Particle Filter for Indoor Robot Localization." In Ambient Intelligence - Software and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19695-4_5.

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Zhao, Qiang, Chen Wei, Liang Qi, and Wenhua Yuan. "Adaptive Double-Resampling Particle Filter Algorithm for Target Tracking." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3187-8_73.

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Nummiaro, Katja, Esther Koller-Meier, and Luc Van Gool. "Object Tracking with an Adaptive Color-Based Particle Filter." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45783-6_43.

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Venkatrayappa, Darshan, Désiré Sidibé, Fabrice Meriaudeau, and Philippe Montesinos. "Adaptive Feature Selection for Object Tracking with Particle Filter." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11755-3_44.

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Xia, Yimin, and Yimin Yang. "Mobile Robot Localization Method Based on Adaptive Particle Filter." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88513-9_103.

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Liu, Feng, Shi-bin Xuan, and Xiang-pin Liu. "Video Target Tracking Based on a New Adaptive Particle Swarm Optimization Particle Filter." In Intelligent Computing Theories and Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39482-9_19.

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Oh, Chi-Min, Yong-Cheol Lee, Ki-Tae Bae, and Chil-Woo Lee. "Adaptive Exemplar-Based Particle Filter for 2D Human Pose Estimation." In Advances in Visual Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_60.

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Mozhdehi, Reza Jalil, Yevgeniy Reznichenko, Abubakar Siddique, and Henry Medeiros. "Convolutional Adaptive Particle Filter with Multiple Models for Visual Tracking." In Advances in Visual Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03801-4_42.

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Conference papers on the topic "Adaptive Particle Filter"

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Guo, Deshu, Aihua Zhang, Di Tian, and Haowen Xia. "An Adaptive Particle Filter Technique for Nonlinear System State Estimation." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010244.

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Yunpeng Li, Lingling Zhao, and Mark Coates. "Particle flow auxiliary particle filter." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383760.

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Li, Qiurong, and Feng Sun. "Adaptive cubature particle filter algorithm." In 2013 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2013. http://dx.doi.org/10.1109/icma.2013.6618110.

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Liverani, Silvia, and Anastasia Papavasiliou. "Entropy Based Adaptive Particle Filter." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378826.

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Liu, Jing, and XiaoChao Li. "Adaptive sparse mixture particle filter." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009621.

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Pal, Soumyasundar, and Mark Coates. "Particle Flow Particle Filter using Gromov's method." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022494.

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Gao, Shi-Wei, Lei Guo, Liang Chen, and Yong Yu. "Adaptive Visual Tracking Using Particle Filter." In 2008 Fifth International Conference on Information Technology: New Generations (ITNG). IEEE, 2008. http://dx.doi.org/10.1109/itng.2008.9.

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Pal, Soumyasundar, and Mark Coates. "Gaussian sum particle flow filter." In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313189.

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Fritsche, Carsten, Thomas B. Schon, and Anja Klein. "The marginalized auxiliary particle filter." In 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2009). IEEE, 2009. http://dx.doi.org/10.1109/camsap.2009.5413276.

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Yuan, Guanglin, Mogen Xue, Pucheng Zhou, and Kai Xie. "PCA-based adaptive particle filter for tracking." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648025.

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Reports on the topic "Adaptive Particle Filter"

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Choudhary, Ruplal, Victor Rodov, Punit Kohli, Elena Poverenov, John Haddock, and Moshe Shemesh. Antimicrobial functionalized nanoparticles for enhancing food safety and quality. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7598156.bard.

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Original objectives The general goal of the project was to utilize the bactericidal potential of curcumin- functionalizednanostructures (CFN) for reinforcement of food safety by developing active antimicrobial food-contact surfaces. In order to reach the goal, the following secondary tasks were pursued: (a) further enhancement of the CFN activity based on understanding their mode of action; (b) preparing efficient antimicrobial surfaces, investigating and optimizing their performance; (c) testing the efficacy of the antimicrobial surfaces in real food trials. Background to the topic The project dealt with reducing microbial food spoilage and safety hazards. Cross-contamination through food-contact surfaces is one of the major safety concerns, aggravated by bacterial biofilm formation. The project implemented nanotech methods to develop novel antimicrobial food-contact materials based on natural compounds. Food-grade phenylpropanoidcurcumin was chosen as the most promising active principle for this research. Major conclusions, solutions, achievements In agreement with the original plan, the following research tasks were performed. Optimization of particles structure and composition. Three types of curcumin-functionalizednanostructures were developed and tested: liposome-type polydiacetylenenanovesicles, surface- stabilized nanoparticles and methyl-β-cyclodextrin inclusion complexes (MBCD). The three types had similar minimal inhibitory concentration but different mode of action. Nanovesicles and inclusion complexes were bactericidal while the nanoparticlesbacteriostatic. The difference might be due to different paths of curcumin penetration into bacterial cell. Enhancing the antimicrobial efficacy of CFN by photosensitization. Light exposure strengthened the bactericidal efficacy of curcumin-MBCD inclusion complexes approximately three-fold and enhanced the bacterial death on curcumin-coated plastic surfaces. Investigating the mode of action of CFN. Toxicoproteomic study revealed oxidative stress in curcumin-treated cells of E. coli. In the dark, this effect was alleviated by cellular adaptive responses. Under light, the enhanced ROS burst overrode the cellular adaptive mechanisms, disrupted the iron metabolism and synthesis of Fe-S clusters, eventually leading to cell death. Developing industrially-feasible methods of binding CFN to food-contact surfaces. CFN binding methods were developed for various substrates: covalent binding (binding nanovesicles to glass, plastic and metal), sonochemical impregnation (binding nanoparticles to plastics) and electrostatic layer-by-layer coating (binding inclusion complexes to glass and plastics). Investigating the performance of CFN-coated surfaces. Flexible and rigid plastic materials and glass coated with CFN demonstrated bactericidal activity towards Gram-negative (E. coli) and Gram-positive (Bac. cereus) bacteria. In addition, CFN-impregnated plastic material inhibited bacterial attachment and biofilm development. Testing the efficacy of CFN in food preservation trials. Efficient cold pasteurization of tender coconut water inoculated with E. coli and Listeriamonocytogeneswas performed by circulation through a column filled with CFN-coated glass beads. Combination of curcumin coating with blue light prevented bacterial cross contamination of fresh-cut melons through plastic surfaces contaminated with E. coli or Bac. licheniformis. Furthermore, coating of strawberries with CFN reduced fruit spoilage during simulated transportation extending the shelf life by 2-3 days. Implications, both scientific and agricultural BARD Report - Project4680 Page 2 of 17 Antimicrobial food-contact nanomaterials based on natural active principles will preserve food quality and ensure safety. Understanding mode of antimicrobial action of curcumin will allow enhancing its dark efficacy, e.g. by targeting the microbial cellular adaptation mechanisms.

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Sela, Hanan, Eduard Akhunov, and Brian J. Steffenson. Population genomics, linkage disequilibrium and association mapping of stripe rust resistance genes in wild emmer wheat, Triticum turgidum ssp. dicoccoides. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598170.bard.

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The primary goals of this project were: (1) development of a genetically characterized association panel of wild emmer for high resolution analysis of the genetic basis of complex traits; (2) characterization and mapping of genes and QTL for seedling and adult plant resistance to stripe rust in wild emmer populations; (3) characterization of LD patterns along wild emmer chromosomes; (4) elucidation of the multi-locus genetic structure of wild emmer populations and its correlation with geo-climatic variables at the collection sites. Introduction In recent years, Stripe (yellow) rust (Yr) caused by Pucciniastriiformis f. sp. tritici(PST) has become a major threat to wheat crops in many parts of the world. New races have overcome most of the known resistances. It is essential, therefore, that the search for new genes will continue, followed by their mapping by molecular markers and introgression into the elite varieties by marker-assisted selection (MAS). The reservoir of genes for disease and pest resistance in wild emmer wheat (Triticumdicoccoides) is an important resource that must be made available to wheat breeders. The majority of resistance genes that were introgressed so far in cultivated wheat are resistance (R) genes. These genes, though confering near-immunity from the seedling stage, are often overcome by the pathogen in a short period after being deployed over vast production areas. On the other hand, adult-plant resistance (APR) is usually more durable since it is, in many cases, polygenic and confers partial resistance that may put less selective pressure on the pathogen. In this project, we have screened a collection of 480 wild emmer accessions originating from Israel for APR and seedling resistance to PST. Seedling resistance was tested against one Israeli and 3 North American PST isolates. APR was tested on accessions that did not have seedling resistance. The APR screen was conducted in two fields in Israel and in one field in the USA over 3 years for a total of 11 replicates. We have found about 20 accessions that have moderate stripe rust APR with infection type (IT<5), and about 20 additional accessions that have novel seedling resistance (IT<3). We have genotyped the collection using genotyping by sequencing (GBS) and the 90K SNP chip array. GBS yielded a total 341K SNP that were filtered to 150K informative SNP. The 90K assay resulted in 11K informative SNP. We have conducted a genome-wide association scan (GWAS) and found one significant locus on 6BL ( -log p >5). Two novel loci were found for seedling resistance. Further investigation of the 6BL locus and the effect of Yr36 showed that the 6BL locus and the Yr36 have additive effect and that the presence of favorable alleles of both loci results in reduction of 2 grades in the IT score. To identify alleles conferring adaption to extreme climatic conditions, we have associated the patterns of genomic variation in wild emmer with historic climate data from the accessions’ collection sites. The analysis of population stratification revealed four genetically distinct groups of wild emmer accessions coinciding with their geographic distribution. Partitioning of genomic variance showed that geographic location and climate together explain 43% of SNPs among emmer accessions with 19% of SNPs affected by climatic factors. The top three bioclimatic factors driving SNP distribution were temperature seasonality, precipitation seasonality, and isothermality. Association mapping approaches revealed 57 SNPs associated with these bio-climatic variables. Out of 21 unique genomic regions controlling heading date variation, 10 (~50%) overlapped with SNPs showing significant association with at least one of the three bioclimatic variables. This result suggests that a substantial part of the genomic variation associated with local adaptation in wild emmer is driven by selection acting on loci regulating flowering. Conclusions: Wild emmer can serve as a good source for novel APR and seedling R genes for stripe rust resistance. APR for stripe rust is a complex trait conferred by several loci that may have an additive effect. GWAS is feasible in the wild emmer population, however, its detection power is limited. A panel of wild emmer tagged with more than 150K SNP is available for further GWAS of important traits. The insights gained by the bioclimatic-gentic associations should be taken into consideration when planning conservation strategies.

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