Effectiveness of Bayesian filters: An information fusion perspective

Li, Tiancheng; Corchado, Juan M.; Bajo, Javier; Sun, Shudong; Paz, Juan F. De

doi:10.1016/j.ins.2015.09.041

Cited by 54 publications

(38 citation statements)

References 44 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A biased conversion degrades the filter performance [53]. The unbias coordinate conversion has also been proposed namely unbiased converted measurement (UCM) in [54], for polar to Cartesian conversion.…”

Section: Appendix a Sensor Measurements Coordinate Conversionsmentioning

confidence: 99%

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Aftab

Hostettler

Freitas

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

Section: Appendix a Sensor Measurements Coordinate Conversionsmentioning

confidence: 99%

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Aftab

Hostettler

Freitas

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…In fact, we did not consider the biasedness of the prior distribution in this paper. The bias of the prior has a significantly negative effect on the posterior, thus the biasedness of the prior distribution will be used in our future work [46]. Moreover, we only used the GMHDD in the post-stack seismic data whose incident and reflection are vertical in this paper.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

View full text Add to dashboard Cite

The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

show abstract

“…In contrast, there are different ways to model the target motion, which rests on the root of different estimation approaches. First, one may infer the state directly from the observation via maximum-likelihood estimation (MLE) or direct observationto-state projection [4]- [6], without relying on any statistical assumptions on the state process. This class of "data-driven" arXiv:1708.02196v1 [stat.AP] 7 Aug 2017 solutions will yield good results when the sensor data are highly informative (namely, the noise is very small), and are gaining favor when little is known about the motion model of the target, or when it is simply not interested/worthwhile or too hard to approximate one.…”

Section: Motivation and Key Contributionmentioning

confidence: 99%

“…However, these residuals are not explicitly available since the true trajectory FoT f k (t) is unknown and is exactly what we want to estimate. As such, we turn to selecting the function that best fits the sensor data series as in (4) in which the fitting residual is defined by the discrepancy between the original sensor data and the pseudo observation made on the FoT of the corresponding time, namely,…”

Section: B Fot Parameter Estimation and Optimizationmentioning

confidence: 99%

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Chen

Sun

et al. 2019

IEEE Trans. Automat. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding timewindow. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

show abstract

Effectiveness of Bayesian filters: An information fusion perspective

Cited by 54 publications

References 44 publications

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Contact Info

Product

Resources

About