A review of resampling techniques in particle filtering framework

Kuptametee, Chanin; Aunsri, Nattapol

doi:10.1016/j.measurement.2022.110836

Cited by 51 publications

(25 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This ensemble of streamflow thus generated is referred to as the "streamflow prediction interval". As the PF technique suffers from the drawback of particle degeneracy which significantly affect the model accuracy (Arulampalam et al, 2002;Elfring et al, 2021;Moradkhani, Hsu, et al, 2005;Plaza Guingla et al, 2013;Weerts & el Serafy, 2006), four resampling techniques, that is, multinomial, residual, systematic and stratified resampling were investigated to assess the best technique (Bolićbolić et al, 2004;Kuptametee & Aunsri, 2022;T. Li et al, 2015;Plaza Guingla et al, 2013).…”

Section: Hbv-pf Modelingmentioning

confidence: 99%

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Roy

Kasiviswanathan

Patidar

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

Section: Hbv-pf Modelingmentioning

confidence: 99%

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Roy

Kasiviswanathan

Patidar

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…where C is the error covariance matrix which describes the discrepancy between the observations and the model variables (we dedicate a detailed discussion in Section 3.1 on the way we estimated this matrix C for our experiments), and T is a simple function that maps the model states into the observation phase space by selecting in the model state in order to drop out the particles far from the observations and duplicate (according to their likelihood) the ones close to the observations (Kuptametee & Aunsri, 2022;Liu & Chen, 1998). The weights 𝐴𝐴 𝐴𝐴 𝑖𝑖 𝑘𝑘+1 of these particles are then all fixed equal to 1/N and the procedure is repeated over time taking at each iteration the new set of particles and weights as the new starting set.…”

Section: Sequential Importance Resampling Particle Filtermentioning

confidence: 99%

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

Jebri

Khodri

2023

J Adv Model Earth Syst

View full text Add to dashboard Cite

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

show abstract

“…In this section, we compared the baseline systematic resampling approach detailed in Alg. 3 against three other resampling methods utilized: multinomial, residual, and stratified (we refer the reader to [21], [29], [30] for a thorough review of these approaches.) Figure 10 presents the ground truth error and filter uncertainty for the four different resampling approaches.…”

Section: A Resampling Scheme Comparisonmentioning

confidence: 99%

ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation

Cauligi¹,

Swan²,

Ono³

et al. 2023

Preprint

View full text Add to dashboard Cite

There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. For example, Endurance-A proposes driving several kilometers a day in order to reach its target traverse of 2000 km in 4 years. Additionally, some of these proposed missions, including Endurance-A and rovers for Permanently Shadowed Regions (PSRs) of the moon, require autonomous driving and absolute localization in darkness. Endurance-A proposes to drive 1200 km of its total traverse at night. The lack of natural light available during these missions limits what can be used as visual landmarks and the range at which landmarks can be observed. In order for planetary rovers to traverse long-ranges, onboard absolute localization is critical to the rover's ability to maintain its planned trajectory and avoid known hazardous regions. Currently, the localization performed onboard rovers is relative to the rover's frame of reference and is performed through the integration of wheel and visual odometry and inertial measurements. To accomplish absolute localization, a "ground-in-the-loop" (GITL) operation is performed wherein a human operator matches local maps or images from onboard with orbital images and maps. This GITL operation places a limit on the distance that can be driven in a day to a few hundred meters, which is the distance that the rover can maintain acceptable localization error via relative methods. Previous work has shown that using craters as landmarks is a promising approach for performing absolute localization on the moon during the day. In this work we present a method of absolute localization that utilizes craters as landmarks and matches detected crater edges on the surface with known craters in orbital maps. We focus on a localization method based on a perception system which has an external illuminator and a stereo camera. While other methods based on lidar exist, lidar is not currently planned for deployment on the current proposed nighttime and PSR missions. In this paper, we evaluate (1) both monocular and stereo based surface crater edge detection techniques, (2) methods of scoring the crater edge matches for optimal localization, and (3) localization performance on simulated Lunar surface imagery at night. We demonstrate that this technique shows promise for maintaining absolute localization error of less than 10 m required for most planetary rover missions.

show abstract

A review of resampling techniques in particle filtering framework

Cited by 51 publications

References 106 publications

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

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

ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation

Contact Info

Product

Resources

About