Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

Li, Tiancheng; Sun, Shudong; Sattar, TP; Corchado, Juan M.

doi:10.1016/j.eswa.2013.12.031

Cited by 204 publications

(106 citation statements)

References 96 publications

(193 reference statements)

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“…This has much the same effect as sample degeneracy, and is more severe when the measurement noise is small. Many works have been devoted to solve this pair of problems, see [3].…”

Section: Introductionmentioning

confidence: 99%

“…As such, the resampling step is often required which reset the particle system in order to solve the degeneracy. But one critical side effect may arise in the meanwhile, namely sample impoverishment, see [2,3] i.e. most particles are of the same state(s) that are duplicated from a few particles while the other particles of small weight are abandoned in the resampling process.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Particle Filter for Indoor Robot Localization

Lang

Villarrubia

et al. 2015

Ambient Intelligence - Software and Applications

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This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach.

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“…This has much the same effect as sample degeneracy, and is more severe when the measurement noise is small. Many works have been devoted to solve this pair of problems, see [3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Particle Filter for Indoor Robot Localization

Lang

Villarrubia

et al. 2015

Ambient Intelligence - Software and Applications

Self Cite

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“…This is often referred to as degeneracy. It has been proven to occur when the particle filter is designed using only the two previously mentioned steps [36][37][38][39]. As the observation processing progresses, the weight variance increases until it gets to a point where the random measure resembles a very poor filtering distribution approximation.…”

Section: The Particle Filtermentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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“…The advantage of this architecture is minimal information loss, but it also raises the problems that all measurements are identically treated at a time and the central filter bears a heavy computational burden, especially in the framework of the PF [18][19][20]. With the development of information fusion, the fusion PF structure employing a bank of local PFs and one master filter is presented and the computational effort is then shared by several filters [21,22].…”

Section: The Particle Filtermentioning

confidence: 99%

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

et al. 2015

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This paper considers the problem of gas turbine transient performance tracking in a cluttered environment. To increase the accuracy and robustness of state estimation, a data-fusion nonlinear estimation method based on an adaptive particle filter (PF) is proposed. This method needs local estimates transmitted to a central filtering unit for data fusion, and then global data feedback to the local PF for consensus propagation. The computational burden is shared by the local PF and central filtering unit in the data-fusion architecture. Furthermore, the PF algorithm used for the data fusion is embedded with the prior knowledge of engine health condition and adaptive to the measurement noise, and hence is called the adaptive PF. The heuristic information of state variables represented by inequality constraints tunes the local estimates by a probability density truncation method. The covariance of measurement noise is calculated by wavelet transform and utilized to update the particle importance function of the real time PF. The performance improvements of the proposed method are indicated through extensive experiments for gradual and abrupt shift performance tracking under conditions of gas turbine transient operation.

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Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

Cited by 204 publications

References 96 publications

An Adaptive Particle Filter for Indoor Robot Localization

An Adaptive Particle Filter for Indoor Robot Localization

Adaptive memory-based single distribution resampling for particle filter

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

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