Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

2011 ◽  
Vol 139 (11) ◽  
pp. 3589-3599 ◽  
Author(s):  
Václav Šmídl ◽  
Radek Hofman
Keyword(s):  
Particle Filtering ◽  
Computational Cost ◽  
Close Relation ◽  
Error Variance ◽  
Environmental Modeling ◽  
Joint Estimation ◽  
Accurate Estimation ◽  
Measurement Error Variance ◽  
Inflation Factor ◽  
Hybrid Filters

Abstract Marginalized particle filtering (MPF), also known as Rao-Blackwellized particle filtering, has been recently developed as a hybrid method combining analytical filters with particle filters. This paper investigates the prospects of this approach in environmental modeling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In the formulation herein, exact marginalization in the MPF is replaced by approximate marginalization, yielding a framework for creation of new hybrid filters. In particular, the authors propose to use the MPF framework for online tuning of nuisance parameters of ensemble filters. Conditional independence–based simplification of the MPF algorithm is proposed for computational reasons and its close relation to previously published methods is discussed. The strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance, and the length scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities, leaving space for further improvements.

scholarly journals The Observational Error of Automated Wind Reports from Aircraft

1986 ◽  
Vol 67 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Lauren L. Morone
Keyword(s):  
Measurement Error ◽  
Structure Function ◽  
Error Variance ◽  
Structure Functions ◽  
Separation Distance ◽  
Observational Error ◽  
Random Measurement Error ◽  
Measurement Error Variance ◽  
Flight Level ◽  
Types Of Error

Data collected from aircraft equipped with AIDS (Aircraft Integrated Data System) instrumentation during the Global Weather Experiment year of 1979 are used to estimate the observational error of winds at flight level from this and other aircraft automated wind-reporting systems. Structure functions are computed from reports that are paired using specific criteria. The value of this function extrapolated to zero separation distance is an estimate of twice the random measurement-error variance of the AIDS-measured winds. Component-wind errors computed in this way range from 2.1 to 3.1 m · s−1 for the two months of data examined, January and August 1979. Observational error, specified in optimum-interpolation analyses to allow the analysis to distinguish among observations of differing quality, is composed of both measurement error and the error of unrepresentativeness. The latter type of error is a function of the resolvable scale of the analysis-prediction system. The structure function, which measures the variability of a field as a function of separation distance, includes both of these types of error. If the resolvable scale of an analysis procedure is known, an estimate of the observational error can be computed from the structure function at that particular distance. An observational error of 5.3 m · s−1 was computed for the u and v wind components for a sample resolvable scale of 300 km. The errors computed from the structure functions are compared to colocation statistics from radiosondes. The errors associated with automated wind reports are found to compare favorably with those estimated for radiosonde winds at that level.

Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA

2011 ◽  
Vol 130-134 ◽  
pp. 3311-3315
Author(s):  
Nai Gao Jin ◽  
Fei Mo Li ◽  
Zhao Xing Li
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Particle Filtering ◽  
Parallel Implementation ◽  
Accurate Estimation ◽  
Quasi Monte Carlo ◽  
Speedup Ratio ◽  
Non Gaussian ◽  
Gpu Implementation ◽  
Number Of Particles

A CUDA accelerated Quasi-Monte Carlo Gaussian particle filter (QMC-GPF) is proposed to deal with real-time non-linear non-Gaussian problems. GPF is especially suitable for parallel implementation as a result of the elimination of resampling step. QMC-GPF is an efficient counterpart of GPF using QMC sampling method instead of MC. Since particles generated by QMC method provides the best-possible distribution in the sampling space, QMC-GPF can make more accurate estimation with the same number of particles compared with traditional particle filter. Experimental results show that our GPU implementation of QMC-GPF can achieve the maximum speedup ratio of 95 on NVIDIA GeForce GTX 460.

Normal Flux Distribution at Step-Lap Joints of Si-Fe Wound Cores

2010 ◽  
Vol 670 ◽  
pp. 284-290 ◽  
Author(s):  
Themistoklis D. Kefalas ◽  
George Loizos ◽  
Antonios G. Kladas
Keyword(s):  
Finite Element ◽  
Flux Distribution ◽  
Computational Cost ◽  
Lap Joints ◽  
Accurate Estimation ◽  
Element Analysis ◽  
Lack Of Information ◽  
Simple Geometry ◽  
Flux Measurements ◽  
Low Computational Cost

Even though, the flux distribution at joints of stacked type transformer cores has been investigated thoroughly many issues remain unclear in the case of wound transformer cores. The paper addresses this lack of information by longitudinal and normal flux measurements at step-lap joints of Si-Fe wound cores. Flux measurements are verified by an original finite element analysis where the necessary excitation is performed by means of a pseudo-source. The advantage of the proposed technique is the accurate estimation of the flux distribution at step-lap joints, with a two dimensional model of simple geometry and low computational cost, by using any commercial finite element code.

scholarly journals Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1445
Author(s):  
Rodi Lykou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
A Priori ◽  
State Space Model ◽  
Computational Cost ◽  
The State ◽  
Observation Equation ◽  
Basic Algorithm ◽  
Space Model ◽  
Observational Errors

Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.

What Doesn’t Get Measured Does Exist: Improving the Accuracy of Computer-Aided Text Analysis

2016 ◽  
Vol 44 (7) ◽  
pp. 2909-2933 ◽  
Author(s):  
Aaron F. McKenny ◽  
Herman Aguinis ◽  
Jeremy C. Short ◽  
Aaron H. Anglin
Keyword(s):  
Measurement Error ◽  
Text Analysis ◽  
Best Practice ◽  
Error Variance ◽  
Theory And Practice ◽  
Future Research ◽  
Specific Factor ◽  
Computer Aided ◽  
Measurement Error Variance ◽  
Reduce Measurement Error

Computer-aided text analysis (CATA) is a form of content analysis that enables the measurement of constructs by processing text into quantitative data based on the frequency of words. CATA has been proposed as a useful measurement approach with the potential to lead to important theoretical advancements. Ironically, while CATA has been offered to overcome some of the known deficiencies in existing measurement approaches, we have lagged behind in regard to assessing the technique’s measurement rigor. Our article addresses this knowledge gap and describes important implications for past as well as future research using CATA. First, we describe three sources of measurement error variance that are particularly relevant to studies using CATA: transient error, specific factor error, and algorithm error. Second, we describe and demonstrate how to calculate measurement error variance with the entrepreneurial orientation, market orientation, and organizational ambidexterity constructs, offering evidence that past substantive conclusions have been underestimated. Third, we offer best-practice recommendations and demonstrate how to reduce measurement error variance by refining existing CATA measures. In short, we demonstrate that although measurement error variance in CATA has not been measured thus far, it does exist and it affects substantive conclusions. Consequently, our article has implications for theory and practice, as well as how to assess and minimize measurement error in future CATA research with the goal of improving the accuracy of substantive conclusions.

scholarly journals Angle Estimation Using Local Searching for Bistatic MIMO Radar with Unknown MCM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chaochen Tang ◽  
Hongbing Qiu ◽  
Xin Liu ◽  
Qinghua Tang
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Mutual Coupling ◽  
Computational Cost ◽  
Estimation Method ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Search Range ◽  
Angle Estimation ◽  
Multiple Input

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity.

scholarly journals Acceleration and Deceleration Rates of Various Vehicle Categories at Signalized Intersections in Mixed Traffic Conditions

2021 ◽  
Vol 49 (4) ◽  
pp. 324-332
Author(s):  
Sushmitha Ramireddy ◽  
Vineethreddy Ala ◽  
Ravishankar KVR ◽  
Arpan Mehar
Keyword(s):  
Close Relation ◽  
Accurate Estimation ◽  
Signal Design ◽  
Driver Behaviour ◽  
Vehicle Type ◽  
Traffic Conditions ◽  
Light Commercial Vehicle ◽  
Acceleration And Deceleration ◽  
Vast Variation ◽  
Two Wheeler

The acceleration and deceleration rates vary from one vehicle type to another. The same vehicle type also exhibits variations in acceleration and deceleration rates due to vast variation in their dynamic and physical characteristics, ratio between weight and power, driver behaviour during acceleration and deceleration manoeuvres. Accurate estimation of acceleration and deceleration rates is very important for proper signal design to ensure minimum control delay for vehicles, which are passing through the intersection. The present study measures acceleration and deceleration rates for four vehicle categories: Two-wheeler, Three-wheeler, Car, and Light Commercial Vehicle (LCV), by using Open Street Map (OSM) tracker mobile application. The acceleration and deceleration rates were measured at 24 signalized intersection approaches in Hyderabad and Warangal cities. The study also developed acceleration and deceleration models for each vehicle type and the developed models were validated based on field data. The results showed that the predicted acceleration and deceleration models showed close relation with those measured in the field. The developed models are useful in predicting average acceleration and deceleration rate for different vehicle types under mixed and poor lane disciplined traffic conditions.

scholarly journals Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

2021 ◽  
Vol 13 (15) ◽  
pp. 2963
Author(s):  
Lifan Sun ◽  
Haofang Yu ◽  
Jian Lan ◽  
Zhumu Fu ◽  
Zishu He ◽  
...  
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
Gaussian Mixture ◽  
Joint Estimation ◽  
Accurate Estimation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Extended Object ◽  
Geometrical Significance

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.

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