Adaptive particle filter based on Kullback–Leibler distance for underwater terrain aided navigation with multi‐beam sonar

Zhou, Tian; Peng, Dongdong; Xu, Chao; Zhang, Wanyuan; Shen, Jiajun

doi:10.1049/iet-rsn.2017.0239

Cited by 30 publications

(15 citation statements)

References 24 publications

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“…While both the PF and the PMF have been successfully demonstrated to handle the problem of terrain navigation, there has been a strong preference towards the use of PFs, which in fact have become the primary choice for addressing the problem of underwater TBN. Multiple authors have adopted the Sequential Importance Resampling (SIR) PF to address underwater TBN, particularly for sensor‐limited systems . However, it is known that in some situations the SIR‐PF can fail, for example if new measurements appear at the tail of the prior distribution, or if the likelihood of the measurements is relatively too peaked.…”

Section: Background and Related Workmentioning

confidence: 99%

A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

Melo

Matos

2019

Asian Journal of Control

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In this article a new Data-Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor-limited AUVs, where typical scenarios often include non-negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data-Driven approach.

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Section: Background and Related Workmentioning

confidence: 99%

A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

Melo

Matos

2019

Asian Journal of Control

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“…PF is independent of the system model, which is used for a variety of non-linear and non-Gaussian models. PF plays an important role in many fields [38][39][40], such as pedestrian tracking, robot navigation and process monitoring. The main bottleneck of PF is the particle impoverishment problem, caused by a reduction in particles.…”

Section: Fpf For Indoor Positioning Systemsmentioning

confidence: 99%

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Chen

Peng

et al. 2018

IJGI

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Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

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“…An auxiliary variable particle filter was used to deal with the second problem [ 10 ], but the filtering performance degrades when the state noise is strong, and because the likelihood function and weight value need to be calculated twice for each particle, the calculation amount increases. Adaptive PF (APF) can release the computation burden by adjusting the number of particles dynamically [ 11 , 12 , 13 , 14 , 15 ]. This method chooses a small particle number if the density is focused on a small part of the state-space, and chooses a large number if the state uncertainty is high [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

Zhong

Cheng

et al. 2020

Sensors

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A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose the cognitive radar tracking method based on a particle filter by completely reconstructing the propagation and the update process with a cognitive structure. Moreover, we develop the cost-reference particle filter based on optimizing the cost function design according to the complicated system or environment with unknown statistics. With this method, the update of the estimation cost and variance arrives at the approximate optimization, and the estimation error can be more adjacent to corresponding low bounds. Simulations about the tracking implementation in unknown noise are utilized to demonstrate the superiority of the proposed algorithm to the existing methods in traditional radar.

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Adaptive particle filter based on Kullback–Leibler distance for underwater terrain aided navigation with multi‐beam sonar

Cited by 30 publications

References 24 publications

A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

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