Journal articles: 'Sequential Monte Carlo Filter'

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Hanif, Ayub, and Robert Elliott Smith. "State Space Modeling & Bayesian Inference with Computational Intelligence." New Mathematics and Natural Computation 11, no. 01 (March 2015): 71–101. http://dx.doi.org/10.1142/s1793005715500040.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.

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Du, Yun Ming, Bing Bing Yan, and Yong Cheng Jiang. "Face Tracking Algorithm Based on Sequential Monte Carlo Filter." Advanced Materials Research 430-432 (January 2012): 1777–81. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1777.

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Incorporating color distribution and spatial layout, this paper proposes a sequential Monte Carlo filter posterior tracking algorithm using color and spatial information in HSV color space. The target model is defined by the spatial color information of the tracking face region. By computing the characteristic distance between sample and target, different weights associated with every sample and the posterior of state vector can be computed. The samples distribution trends to the state distribution, whose validity is guaranteed by the strong law of large numbers. The tracking results using weighted samples are given in simulation. Experimental results show the probabilistic approach is simple and computationally efficient. In addition, this algorithm based on the sequential Monte Carlo filter could predict the location of face and track its trajectory satisfactorily in various complex conditions.

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Kitagawa, Genshiro. "Computational aspects of sequential Monte Carlo filter and smoother." Annals of the Institute of Statistical Mathematics 66, no. 3 (March 4, 2014): 443–71. http://dx.doi.org/10.1007/s10463-014-0446-0.

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Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong, and Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.

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As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.

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Freitas, J. F. G. de, M. Niranjan, A. H. Gee, and A. Doucet. "Sequential Monte Carlo Methods to Train Neural Network Models." Neural Computation 12, no. 4 (April 1, 2000): 955–93. http://dx.doi.org/10.1162/089976600300015664.

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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.

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Hong Yoon, Ju, Du Yong Kim, and Kuk-Jin Yoon. "Efficient importance sampling function design for sequential Monte Carlo PHD filter." Signal Processing 92, no. 9 (September 2012): 2315–21. http://dx.doi.org/10.1016/j.sigpro.2012.01.010.

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Pulido, Manuel, and Peter Jan van Leeuwen. "Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter." Journal of Computational Physics 396 (November 2019): 400–415. http://dx.doi.org/10.1016/j.jcp.2019.06.060.

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Beskos, Alexandros, Dan Crisan, Ajay Jasra, Kengo Kamatani, and Yan Zhou. "A stable particle filter for a class of high-dimensional state-space models." Advances in Applied Probability 49, no. 1 (March 2017): 24–48. http://dx.doi.org/10.1017/apr.2016.77.

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Abstract We consider the numerical approximation of the filtering problem in high dimensions, that is, when the hidden state lies in ℝd with large d. For low-dimensional problems, one of the most popular numerical procedures for consistent inference is the class of approximations termed particle filters or sequential Monte Carlo methods. However, in high dimensions, standard particle filters (e.g. the bootstrap particle filter) can have a cost that is exponential in d for the algorithm to be stable in an appropriate sense. We develop a new particle filter, called the space‒time particle filter, for a specific family of state-space models in discrete time. This new class of particle filters provides consistent Monte Carlo estimates for any fixed d, as do standard particle filters. Moreover, when there is a spatial mixing element in the dimension of the state vector, the space‒time particle filter will scale much better with d than the standard filter for a class of filtering problems. We illustrate this analytically for a model of a simple independent and identically distributed structure and a model of an L-Markovian structure (L≥ 1, L independent of d) in the d-dimensional space direction, when we show that the algorithm exhibits certain stability properties as d increases at a cost 𝒪(nNd2), where n is the time parameter and N is the number of Monte Carlo samples, which are fixed and independent of d. Our theoretical results are also supported by numerical simulations on practical models of complex structures. The results suggest that it is indeed possible to tackle some high-dimensional filtering problems using the space‒time particle filter that standard particle filters cannot handle.

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Ahmed, Imtiaz. "Dolphin Whistle Track Estimation Using Sequential Monte Carlo Probability Hypothesis Density Filter." Dhaka University Journal of Science 62, no. 1 (February 7, 2015): 17–20. http://dx.doi.org/10.3329/dujs.v62i1.21954.

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This article focuses on possible automation of dolphin whistle track estimation process within the context of Multiple Target Tracking (MTT). It provides automatic whistle track estimation from raw hydrophone measurements using the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. Hydrophone measurements for three different types of species namely bottlenose dolphin (Tursiops truncates), common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba) have been used to benchmark the tracking performance of the SMC-PHD filter against three major challenges- the presence of multiple whistles, spontaneous death/birth of whistles and multiple whistles crossing each other. Quantitative analysis of the whistle track estimation accuracy is not possible since there is no ground truth type track for the dolphin whistles. Hence visual inspection of estimated tracks has been used corroborate the satisfactory tracking performance in the presence of all three challenges. DOI: http://dx.doi.org/10.3329/dujs.v62i1.21954 Dhaka Univ. J. Sci. 62(1): 17-20, 2014 (January)

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Thulin, Kristian, Geir Nævdal, Hans Julius Skaug, and Sigurd Ivar Aanonsen. "Quantifying Monte Carlo Uncertainty in the Ensemble Kalman Filter." SPE Journal 16, no. 01 (October 27, 2010): 172–82. http://dx.doi.org/10.2118/123611-pa.

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Summary The ensemble Kalman filter (EnKF) is currently considered one of the most promising methods for conditioning reservoir-simulation models to production data. The EnKF is a sequential Monte Carlo method based on a low-rank approximation of the system covariance matrix. The posterior probability distribution of model variables may be estimated from the updated ensemble, but, because of the low-rank covariance approximation, the updated ensemble members become correlated samples from the posterior distribution. We suggest using multiple EnKF runs, each with a smaller ensemble size, to obtain truly independent samples from the posterior distribution. This allows a pointwise confidence interval to be constructed for the posterior cumulative distribution function (CDF). We investigate the methodology for finding an optimal combination of ensemble batch size n and number of EnKF runs m while keeping the total number of ensemble members n×m constant. The optimal combination of n and m is found through minimizing the integrated mean-square error (MSE) for the CDFs. We illustrate the approach on two models, first a small linear model and then a synthetic 2D model inspired by petroleum applications. In the latter case, we choose to define an EnKF run with 10,000 ensemble members as having zero Monte Carlo error. The proposed methodology should be applicable also to larger, more-realistic models.

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Heine, Kari, and Dan Crisan. "Uniform approximations of discrete-time filters." Advances in Applied Probability 40, no. 04 (December 2008): 979–1001. http://dx.doi.org/10.1017/s0001867800002937.

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Throughout recent years, various sequential Monte Carlo methods, i.e. particle filters, have been widely applied to various applications involving the evaluation of the generally intractable stochastic discrete-time filter. Although convergence results exist for finite-time intervals, a stronger form of convergence, namely, uniform convergence, is required for bounding the error on an infinite-time interval. In this paper we prove easily verifiable conditions for the filter applications that are sufficient for the uniform convergence of certain particle filters. Essentially, the conditions require the observations to be accurate enough. No mixing or ergodicity conditions are imposed on the signal process.

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Heine, Kari, and Dan Crisan. "Uniform approximations of discrete-time filters." Advances in Applied Probability 40, no. 4 (December 2008): 979–1001. http://dx.doi.org/10.1239/aap/1231340161.

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Throughout recent years, various sequential Monte Carlo methods, i.e. particle filters, have been widely applied to various applications involving the evaluation of the generally intractable stochastic discrete-time filter. Although convergence results exist for finite-time intervals, a stronger form of convergence, namely, uniform convergence, is required for bounding the error on an infinite-time interval. In this paper we prove easily verifiable conditions for the filter applications that are sufficient for the uniform convergence of certain particle filters. Essentially, the conditions require the observations to be accurate enough. No mixing or ergodicity conditions are imposed on the signal process.

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Noh, S. J., Y. Tachikawa, M. Shiiba, and S. Kim. "Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization." Hydrology and Earth System Sciences Discussions 8, no. 2 (April 4, 2011): 3383–420. http://dx.doi.org/10.5194/hessd-8-3383-2011.

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Abstract. Applications of data assimilation techniques have been widely used to improve hydrologic prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", provide the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response time of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on Markov chain Monte Carlo (MCMC) is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, WEP is implemented for the sequential data assimilation through the updating of state variables. Particle filtering is parallelized and implemented in the multi-core computing environment via open message passing interface (MPI). We compare performance results of particle filters in terms of model efficiency, predictive QQ plots and particle diversity. The improvement of model efficiency and the preservation of particle diversity are found in the lagged regularized particle filter.

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Finke, Axel, Arnaud Doucet, and Adam M. Johansen. "Limit theorems for sequential MCMC methods." Advances in Applied Probability 52, no. 2 (June 2020): 377–403. http://dx.doi.org/10.1017/apr.2020.9.

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AbstractBoth sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants. While SMC methods sample particles conditionally independently at each time step, sequential MCMC methods sample particles according to a Markov chain Monte Carlo (MCMC) kernel. Introduced over twenty years ago in [6], sequential MCMC methods have attracted renewed interest recently as they empirically outperform SMC methods in some applications. We establish an $\mathbb{L}_r$ -inequality (which implies a strong law of large numbers) and a central limit theorem for sequential MCMC methods and provide conditions under which errors can be controlled uniformly in time. In the context of state-space models, we also provide conditions under which sequential MCMC methods can indeed outperform standard SMC methods in terms of asymptotic variance of the corresponding Monte Carlo estimators.

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Hiranmayi, Penumarty, Kola Sai Gowtham, S. Koteswara Rao, and V. Gopi Tilak. "Tracking of pendulum using particle filter with residual resampling." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 12. http://dx.doi.org/10.14419/ijet.v7i2.7.10246.

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The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consist of discrete state components since under these circumstances the Bayesian approximations do not always work well.

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Lian, Feng, Chen Li, Chongzhao Han, and Hui Chen. "Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters." Journal of Applied Mathematics 2012 (2012): 1–25. http://dx.doi.org/10.1155/2012/584140.

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The convergence for the sequential Monte Carlo (SMC) implementations of the multitarget multi-Bernoulli (MeMBer) filter and cardinality-balanced MeMBer (CBMeMBer) filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

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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 (July 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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Noh, S. J., Y. Tachikawa, M. Shiiba, and S. Kim. "Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization." Hydrology and Earth System Sciences 15, no. 10 (October 25, 2011): 3237–51. http://dx.doi.org/10.5194/hess-15-3237-2011.

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Abstract. Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.

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Kim, Sangil, and Jeong-Soo Park. "Sequential Monte Carlo filters for abruptly changing state estimation." Probabilistic Engineering Mechanics 26, no. 2 (April 2011): 194–201. http://dx.doi.org/10.1016/j.probengmech.2010.07.010.

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Peralta-Cabezas, J. L., M. Torres-Torriti, and M. Guarini-Hermann. "A comparison of Bayesian prediction techniques for mobile robot trajectory tracking." Robotica 26, no. 5 (September 2008): 571–85. http://dx.doi.org/10.1017/s0263574708004153.

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SUMMARYThis paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

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Infante, Saba, Luis Sánchez, Aracelis Hernández, and José Marcano. "Sequential Monte Carlo Filters with Parameters Learning for Commodity Pricing Models." Statistics, Optimization & Information Computing 9, no. 3 (June 22, 2021): 694–716. http://dx.doi.org/10.19139/soic-2310-5070-814.

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In this article, an estimation methodology based on the sequential Monte Carlo algorithm is proposed, thatjointly estimate the states and parameters, the relationship between the prices of futures contracts and the spot prices of primary products is determined, the evolution of prices and the volatility of the historical data of the primary market (Gold and Soybean) are analyzed. Two stochastic models for an estimate the states and parameters are considered, the parameters and states describe physical measure (associated with the price) and risk-neutral measure (associated with the markets to futures), the price dynamics in the short-term through the reversion to the mean and volatility are determined, while that in the long term through markets to futures. Other characteristics such as seasonal patterns, price spikes, market dependent volatilities, and non-seasonality can also be observed. In the methodology, a parameter learning algorithm is used, specifically, three algorithms are proposed, that is the sequential Monte Carlo estimation (SMC) for state space modelswith unknown parameters: the first method is considered a particle filter that is based on the sampling algorithm of sequential importance with resampling (SISR). The second implemented method is the Storvik algorithm [19], the states and parameters of the posterior distribution are estimated that have supported in low-dimensional spaces, a sufficient statistics from the sample of the filtered distribution is considered. The third method is (PLS) Carvalho’s Particle Learning and Smoothing algorithm [31]. The cash prices of the contracts with future delivery dates are analyzed. The results indicate postponement of payment, the future prices on different maturity dates with the spot price are highly correlated. Likewise, the contracts with a delivery date for the last periods of the year 2017, the spot price lower than the prices of the contracts with expiration date for 12 and 24 months is found, opposite occurs in the contracts with expiration date for 1 and 6 months.

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Song, Bin, Enqi Liang, and Bing Liu. "American Option Pricing Using Particle Filtering Under Stochastic Volatility Correlated Jump Model." Journal of Systems Science and Information 2, no. 6 (December 25, 2014): 505–19. http://dx.doi.org/10.1515/jssi-2014-0505.

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AbstractA particle filter based method to price American option under partial observation framework is introduced. Assuming the underlying price process is driven by unobservable latent factors, the pricing methodology should contain inference on latent factors in addition to the original least-squares Monte Carlo approach of Longstaff and Schwartz. Sequential Monte Carlo is a widely applied technique to provide such inference. Applications on stochastic volatility models has been introduced by Rambharat, who assume that volatility is a latent stochastic process, and capture information about it using particle filter based “summary vectors”. This paper investigates this particle filter based pricing methodology, with an extension to a stochastic volatility jump model, stochastic volatility correlated jump model (SVCJ), and auxiliary particle filter (APF) introduced first by Pitt and Shephard. In the APF algorithm of SVCJ model, it also provides a modification version to enhance the performance in the resampling step. A detailed implementation and numerical examples of the algorithm are provided. The algorithm is also applied to empirical data.

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Jiang, Tong-yang, Mei-qin Liu, Xie Wang, and Sen-lin Zhang. "An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering." Journal of Zhejiang University SCIENCE C 15, no. 6 (June 2014): 445–57. http://dx.doi.org/10.1631/jzus.c1400025.

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Li, Jiahao, Joaquin Klee Barillas, Clemens Guenther, and Michael A. Danzer. "Multicell state estimation using variation based sequential Monte Carlo filter for automotive battery packs." Journal of Power Sources 277 (March 2015): 95–103. http://dx.doi.org/10.1016/j.jpowsour.2014.12.010.

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Shi, Shengxian, and Daoyi Chen. "Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter." Flow Measurement and Instrumentation 22, no. 3 (June 2011): 190–200. http://dx.doi.org/10.1016/j.flowmeasinst.2011.02.001.

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Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets." Journal of Applied Mathematics 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/727430.

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By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.

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Qi, Wen Juan, Peng Zhang, Zi Li Deng, and Yuan Gao. "Multisensor Covariance Intersection Fusion Kalman Filters." Applied Mechanics and Materials 373-375 (August 2013): 946–52. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.946.

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For multisensor system with colored measurement noises, the common disturbance noises and measurement biases, the batch covariance intersection fusion (BCI) Kalman filter and the sequential covariance intersection fusion (SCI) Kalman filter are presented, which can avoid the computation of the local filtering errors and reduce the computational burden significantly. Under the linear unbiased minimum variance (ULMV) criterion, the three weighted fusion Kalman filters (weighted by matrices, scalars or diagonal matrices) are also presented. Their accuracy relations are analyzed and compared. Specially, the accuracy of the proposed covariance intersection fusion Kalman filters are higher than that of each local Kalman filters, and is lower than that of optimal fuser weighted by matrices. The geometric interpretation of the accuracy relations is given by the covariance ellipses. A Monte-Carlo simulation example for a tracking system verifies the correctness of the theoretical accuracy relations.

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Ergun, Ayla, Riccardo Barbieri, Uri T. Eden, Matthew A. Wilson, and Emery N. Brown. "Construction of Point Process Adaptive Filter Algorithms for Neural Systems Using Sequential Monte Carlo Methods." IEEE Transactions on Biomedical Engineering 54, no. 3 (March 2007): 419–28. http://dx.doi.org/10.1109/tbme.2006.888821.

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Danis, F. Serhan, A. Taylan Cemgil, and Cem Ersoy. "Adaptive Sequential Monte Carlo Filter for Indoor Positioning and Tracking With Bluetooth Low Energy Beacons." IEEE Access 9 (2021): 37022–38. http://dx.doi.org/10.1109/access.2021.3062818.

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Kamsing, Patcharin, Peerapong Torteeka, Wuttichai Boonpook, and Chunxiang Cao. "Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition." Applied Computational Intelligence and Soft Computing 2020 (October 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/8866259.

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To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.

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Stroud, Jonathan R., and Thomas Bengtsson. "Sequential State and Variance Estimation within the Ensemble Kalman Filter." Monthly Weather Review 135, no. 9 (September 1, 2007): 3194–208. http://dx.doi.org/10.1175/mwr3460.1.

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Abstract Kalman filter methods for real-time assimilation of observations and dynamical systems typically assume knowledge of the system parameters. However, relatively little work has been done on extending state estimation procedures to include parameter estimation. Here, in the context of the ensemble Kalman filter, a Monte Carlo–based algorithm is proposed for sequential estimation of the states and an unknown scalar observation variance. A Bayesian approach is adopted that yields analytical updating of the parameter distribution and provides samples from the posterior distribution of the states and parameters. The proposed assimilation algorithm extends standard ensemble methods, including perturbed observations, and serial and square root assimilation schemes. The method is illustrated on the Lorenz 40-variable system and is shown to be robust with system nonlinearities, sparse observation networks, and the choice of the initial parameter distribution.

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Cemgil, A. T., and B. Kappen. "Monte Carlo Methods for Tempo Tracking and Rhythm Quantization." Journal of Artificial Intelligence Research 18 (January 1, 2003): 45–81. http://dx.doi.org/10.1613/jair.1121.

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We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.

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Zhang, Jungen. "Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter." International Journal of Circuits, Systems and Signal Processing 14 (January 13, 2021): 1129–36. http://dx.doi.org/10.46300/9106.2020.14.141.

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Following Mahler’s framework forinformation fusion, this paper develops a implementationof cardinalized probability hypothesis density (CPHD)filter for bearings-only multitarget tracking.Rao-Blackwellized method is introduced in the CPHDfiltering framework for mixed linear/nonlinear state spacemodels. The sequential Monte Carlo (SMC) method is usedto predict and estimate the nonlinear state of targets.Kalman filter (KF) is adopted to estimate the linear stateswith the information embedded in the estimated nonlinearstates. The multitarget state estimates are extracted byutilizing the kernel density estimation (KDE) theory andmean-shift algorithm to enhance tracking performance.Moreover, the computational load of the filter is analyzedby introducing equivalent flop measure. Finally, theperformance of the proposed Rao-Blackwellized particleCPHD filter is evaluated through a challengingbearings-only multitarget tracking simulation experiment.

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Wang, Yiwen, António R. C. Paiva, José C. Príncipe, and Justin C. Sanchez. "Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces." Neural Computation 21, no. 10 (October 2009): 2894–930. http://dx.doi.org/10.1162/neco.2009.01-08-699.

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Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. We have also proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. This letter presents a systematic testing of this algorithm in a simulated neural spike train decoding experiment and then in BMI data. Compared to a point-process adaptive filtering algorithm with a linear observation model and a gaussian approximation (the counterpart for point processes of the Kalman filter), our sequential Monte Carlo estimation methodology exploits a detailed encoding model (tuning function) derived for each neuron from training data. However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.

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Iltis, R. A. "A sequential monte carlo filter for joint linear/nonlinear state estimation with application to DS-CDMA." IEEE Transactions on Signal Processing 51, no. 2 (February 2003): 417–26. http://dx.doi.org/10.1109/tsp.2002.806995.

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Ma, Dongdong, Feng Lian, and Jing Liu. "Sequential Monte Carlo implementation of cardinality balanced multi‐target multi‐Bernoulli filter for extended target tracking." IET Radar, Sonar & Navigation 10, no. 2 (February 2016): 272–77. http://dx.doi.org/10.1049/iet-rsn.2015.0081.

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Barbary, Mohamed, and Mohamed H. Abd El-Azeem. "Track-before-detect for complex extended targets based sequential monte carlo Mb-sub-random matrices filter." Multidimensional Systems and Signal Processing 32, no. 3 (February 6, 2021): 863–96. http://dx.doi.org/10.1007/s11045-021-00762-3.

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Martínez-Barberá, Humberto, Pablo Bernal-Polo, and David Herrero-Pérez. "Sensor Modeling for Underwater Localization Using a Particle Filter." Sensors 21, no. 4 (February 23, 2021): 1549. http://dx.doi.org/10.3390/s21041549.

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This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

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Jamal, Alaa, and Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model." Agriculture 10, no. 12 (December 7, 2020): 606. http://dx.doi.org/10.3390/agriculture10120606.

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Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

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Ma, Junkai, Haibo Luo, Bin Hui, and Zheng Chang. "Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo." Sensors 17, no. 3 (March 4, 2017): 512. http://dx.doi.org/10.3390/s17030512.

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Kang, Chang Ho, and Chan Gook Park. "Particles resampling scheme using regularized optimal transport for sequential Monte Carlo filters." International Journal of Adaptive Control and Signal Processing 32, no. 10 (August 2, 2018): 1393–402. http://dx.doi.org/10.1002/acs.2918.

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Li, Jiahao, Joaquin Klee Barillas, Clemens Guenther, and Michael A. Danzer. "Sequential Monte Carlo filter for state estimation of LiFePO 4 batteries based on an online updated model." Journal of Power Sources 247 (February 2014): 156–62. http://dx.doi.org/10.1016/j.jpowsour.2013.08.099.

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Zhang, Jian, and Ling Shen. "Applied Technology in an Adaptive Particle Filter Based on Interval Estimation and KLD-Resampling." Advanced Materials Research 1014 (July 2014): 452–58. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.452.

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Particle filter as a sequential Monte Carlo method is widely applied in stochastic sampling for state estimation in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on the number of particles and the relocating method. The automatic selection of sample size for a given task is therefore essential for reducing unnecessary computation and for optimal performance, especially when the posterior distribution greatly varies overtime. This paper presents an adaptive resampling method (IE_KLD_PF) based on interval estimation, and after interval estimating the expectation of the system states, the new algorithm adopts Kullback-Leibler distance (KLD) to determine the number of particles to resample from the interval and update the filter results by current observation information. Simulations are performed to show that the proposed filter can reduce the average number of samples significantly compared to the fixed sample size particle filter.

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Gong, Yang, and Chen Cui. "A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise." Sensors 21, no. 11 (May 22, 2021): 3611. http://dx.doi.org/10.3390/s21113611.

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In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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Wu, Wei Hua, Jing Jiang, Chong Yang Liu, and Xiong Hua Fan. "Fast Gaussian Mixture Probability Hypothesis Density Filter." Applied Mechanics and Materials 568-570 (June 2014): 550–56. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.550.

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Although the Gaussian mixture probability hypothesis density (GMPHD) filter is a multi-target tracker that can alleviate the computational intractability of the optimal multi-target Bayes filter and its computational complex is lower than that of sequential Monte Carlo probability hypothesis density (SMCPHD), its computational burden can be reduced further. In the standard GMPHD filter, each observation should be matched with each component when the PHD is updated. In practice, time cost of evaluating many unlikely measurements-to-components parings is wasteful, because their contribution is very limited. As a result, a substantial reduction in complexity could be obtained by directly setting relative value associated with these parings. A fast GMPHD algorithm is proposed in the paper based on gating strategy. Simulation results show that the fast GMPHD can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.

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Wang, Sen, Qinglong Bao, and Zengping Chen. "Refined PHD Filter for Multi-Target Tracking under Low Detection Probability." Sensors 19, no. 13 (June 26, 2019): 2842. http://dx.doi.org/10.3390/s19132842.

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Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.

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Wang, Dong, Shilong Sun, and Peter W. Tse. "A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features." Mechanical Systems and Signal Processing 52-53 (February 2015): 293–308. http://dx.doi.org/10.1016/j.ymssp.2014.07.005.

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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 (May 28, 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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Poterjoy, Jonathan, Louis Wicker, and Mark Buehner. "Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction." Monthly Weather Review 147, no. 4 (March 20, 2019): 1107–26. http://dx.doi.org/10.1175/mwr-d-17-0344.1.

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Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

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Dong, Guangzhong, Zonghai Chen, and Jingwen Wei. "Sequential Monte Carlo Filter for State-of-Charge Estimation of Lithium-Ion Batteries Based on Auto Regressive Exogenous Model." IEEE Transactions on Industrial Electronics 66, no. 11 (November 2019): 8533–44. http://dx.doi.org/10.1109/tie.2018.2890499.

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Journal articles on the topic 'Sequential Monte Carlo Filter'

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