Observation noise modeling based particle filter: An efficient algorithm for target tracking in glint noise environment

Du, Hangyuan; Wang, Wenjian; Bai, Liang

doi:10.1016/j.neucom.2015.01.057

Cited by 20 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For successful VB inference the choice of the parameters θ i k and ε is important. Note that in (19) we cannot set θ i k equal to 0 or 1 and ε as exactly 0. Otherwise, the parameter Ω i k becomes independent of W k , i.e.…”

Section: Choice Of the Parameters θ I K And εmentioning

confidence: 99%

“…The proposed SOR filter 20) and ( 21); Initialize θ i k , the convergence threshold τ, δ = τ + 1, the iteration index l = 12) and (13); while δ > τ do Update W ii k (l) with (15) and 19) for each i;…”

Section: Algorithmmentioning

confidence: 99%

“…However, since these methods are based on static loss functions based on design parameters, meticulous tuning of these parameters is required [17]. Therefore, tuning-free learning-based compensation approaches have also been advocated in the literature [17][18][19][20]. These methods assume a distribution to describe the measurement noise and subsequently aim to learn the parameters of the distribution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

Chughtai¹,

Tahir²,

Uppal³

2021

Preprint

View full text Add to dashboard Cite

This letter presents a novel outlier-robust filter for nonlinear dynamical systems. We consider a common case where measurements are obtained from independent sensors. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted observations during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to similar baseline methods.

show abstract

Section: Choice Of the Parameters θ I K And εmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

Chughtai¹,

Tahir²,

Uppal³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Since bearing measurements have a nonlinear relationship with the state of the target, vast literature considers adopting EKF and PF in some bearing-only tracking cases [ 84 , 85 , 86 ]. However, in practice, underwater object maneuvering can be described as a simple linear model because the angle changes little during the sampling interval for the limited speed of the target compared with the long distance between objects and the receivers.…”

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

Underwater Acoustic Target Tracking: A Review

Luo

Han

Fan

2018

Sensors

114

View full text Add to dashboard Cite

Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper.

show abstract

“…GSFs are based on the idea that a non-Gaussian probability density function (PDF) can be approximated by a sum of Gaussian PDFs [ 15 ]. Based on Bayesian estimation and a sequential Monte Carlo approach, Du et al utilized PF to handle nonlinear and non-Gaussian problems, and the PF is applied in small target tracking in an optimal image sequence [ 16 ]. However, these nonlinear filters were susceptible to outliers and did not have robust property.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion

Deng

Yin

Huo

et al. 2018

Sensors

View full text Add to dashboard Cite

In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors’ data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.

show abstract

Observation noise modeling based particle filter: An efficient algorithm for target tracking in glint noise environment

Cited by 20 publications

References 41 publications

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

Underwater Acoustic Target Tracking: A Review

Adaptive Robust Unscented Kalman Filter via Fading Factor and Maximum Correntropy Criterion

Contact Info

Product

Resources

About