Log-PF: Particle Filtering in Logarithm Domain

Gentner, Christian; Zhang, Siwei; Jost, Thomas

doi:10.1155/2018/5763461

Cited by 7 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With all building blocks been introduced, we can finally describe the overall DiPNet algorithm for an agent a u in Algorithm 1. For numerical stability, DiPNet is operated in logarithm domain with Jacobian algorithm as described in [56]. DiPNet only requests evaluating cross-correlation func-…”

Section: B Dipnet With Ofdm Waveformmentioning

confidence: 99%

Distributed Direct Localization Suitable for Dense Networks

Zhang

Staudinger

Jost

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

Traditional network localization algorithms contain ranging and localization steps, which have systematic disadvantages. We propose an algorithm dubbed direct particle filter based distributed network localization (DiPNet). A node's location is directly estimated from the received signals, incorporating location uncertainty of neighboring nodes. The propagation effects on DiPNet become insignificant for dense networks, due to the massive-link collective physical layer processing. DiPNet achieves a near-optimal performance with low complexity, which is particularly attractive for realtime dense-network localization.

show abstract

Section: B Dipnet With Ofdm Waveformmentioning

confidence: 99%

Distributed Direct Localization Suitable for Dense Networks

Zhang

Staudinger

Jost

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…So far, this model was printed and then placed on the bone, which is expensive and time consuming. In a new approach [ 29 ], a robotic arm is equipped with a depth camera to track a representation of the template in the depth image while using a particle filter [ 23 ], and a projector to display cutting outlines onto the bone ( Figure 2 ) during surgery. However, this new approach leads to the risk that the projection is off when the tracking is inaccurate.…”

Section: Methodsmentioning

confidence: 99%

“…We consider this detection mechanism, which is also investigated by Chow [ 22 ] in a more general setting, as a baseline in our evaluation. In this contribution, we investigate the challenge of how to equip a recent state-of-the-art particle filtering technology for object tracking, which is based on numerically stable representations of particle weights in the logarithmic domain [ 23 ], by efficient possibilities to equip the tracking result with a notion of reliability in the form of a reject option, which filters too large deviations of the tracked quantities from their true values. We investigate this, in particular, with respect to changing configuration settings of the particle filter.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Kummert

Schulz

Redick

et al. 2021

Sensors

View full text Add to dashboard Cite

Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain—object tracking in assisted surgery in the domain of Robotic Osteotomies—that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.

show abstract

“…The proposed filter is used to model the probability density of the state vector X k = [x k , y k , θ k ] T at step k by N p particles. According to [38]- [40] and assuming a first-order hidden Markov model, the posterior filtered density p x k |z 0:k is approximated by:…”

Section: ) Tum Teammentioning

confidence: 99%

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

Renaudin¹,

Ortiz²,

Perul³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future.

show abstract

Log-PF: Particle Filtering in Logarithm Domain

Cited by 7 publications

References 17 publications

Distributed Direct Localization Suitable for Dense Networks

Distributed Direct Localization Suitable for Dense Networks

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

Contact Info

Product

Resources

About