Gaussian sum particle filtering

Kotecha, J.H.; Djurić, Petar M.

doi:10.1109/tsp.2003.816754

Cited by 433 publications

(227 citation statements)

References 24 publications

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“…However, a major disadvantage of SISR is the computational complexity, a large part of which comes from a procedure called resampling [85]. The Gaussian sum particle filter (GSPF) [85] implements the PF assuming Gaussian mixture distributions for the system and measurement noises. The GPF [85] is quite similar to the SISR filter by the fact that importance sampling is used to obtain particles.…”

Section: ) Particle Filteringmentioning

confidence: 99%

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A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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Section: ) Particle Filteringmentioning

confidence: 99%

“…The Gaussian sum particle filter (GSPF) [85] implements the PF assuming Gaussian mixture distributions for the system and measurement noises. The GPF [85] is quite similar to the SISR filter by the fact that importance sampling is used to obtain particles. However, unlike the SISR filters, resampling is not required in the GPF.…”

Section: ) Particle Filteringmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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“…In [45], Stordal et al identify a connection between the EnKF and the Gaussian mixture filter [46,47]. The EnKF is viewed as a special case of Gaussian mixture filter with the added constraint that all the weights are uniform.…”

Section: Enkf With Gaussian Mixture Modelsmentioning

confidence: 99%

Distributed ensemble Kalman filtering

Shahid

Üstebay

Coates

2014

2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Distributed estimation in a wireless sensor network has many advantages. It eliminates the need of the centralized knowledge of the measurement model parameters and does not have a single point of failure. Also, any sensor node can be queried to retrieve the state estimate. Furthermore, for high dimensional measurements, local processing of information also results in a significant reduction in the communication overhead.The existing distributed Kalman filtering schemes have good computational and communication efficiency, but do not work well for non-linear and non-Gaussian problems. On the other hand, the distributed particle filtering schemes handle the non-linearities well, but have a much higher computational and communication cost.In this thesis, we propose novel distributed filtering schemes based on the ensemble Kalman filter (EnKF). We consider three forms of the EnKF and express their update equations in an alternative information form. This allows us to use the randomized gossip algorithm to reach consensus on the sufficient statistics and perform local updates. The simulation results show that all three forms of the EnKF have a considerably lower computational cost compared to the particle filters. The results suggest that the proposed distributed schemes outperform the existing state-of-the-art distributed filtering schemes in two scenarios, a) linear measurement model with non-linear state dynamics, and b) high dimensional measurements (model parameters known everywhere in the network) with nonlinear measurement model and state dynamics. In both of these scenarios, the proposed schemes achieve an estimation accuracy comparable to the existing state-of-the-art schemes while significantly reducing the communication cost.ii Résumé L'estimation distribuée dans un réseau de capteurs sans fil possède plusieurs avantages. Ellé elimine le besoin d'une connaissance centralisée des paramtres du modèle de mesure et n'a pas un point de défaillance unique. Aussi, n'importe quel agent-capteur peut-être consulté pour obtenir une approximation de l'état général. De plus, pour des mesures de hautes dimensions, le calcul local d'informations résulte en une réduction significative des coûts de communication.Les implémentations courantes du filtre de Kalman sont efficaces sur les plans de la charge de calcul et de la communication mais ne le sont pas pour les problmes non-linéaires et non-gaussiens. D'un autre côté, les techniques distribuées de filtrage particulaire gèrent avec succès les cas non-linéaires mais sont coûteuses sur les plans de la charge de calcul et de la communication.Dans cette thèse, nous proposons des techniques de filtrage distribué basées sur le filtre de Kalman d'ensemble (FKEn). Nous considérons trois formes du FKEn et exprimons leurséquations de changement sous une forme alternative. Cela nous permet d'utiliser un algorithme de gossip aléatoire afin d'atteindre un consensus sur les statistiques suffisantes et calculer les changements locaux. Les résultats des simulations montrent que ...

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“…The next three subsections are based on methods for calculating the predicted mean and covariance, in equations (7) and (8), and then computing (14)(15)(16)(17)(18) in order to compute the mean and covariance update, in equations (12) and (13). The technique presented in the final subsection takes a different approach in that it attempts to approximate the Gaussian Bayes recursion, in equations (5) and (6), directly through Monte Carlo integration and importance sampling.…”

Section: Gaussian Filteringmentioning

confidence: 99%

“…This approach has been extended for more general probability distributions [17] within a Gaussian sum framework, and we have shown that this approach can be extended to intensity functions [18]. Suppose that the Gaussian posterior density, N (ξ; m k−1 , P k−1 ), is projected through nonlinear state function ϕ k−1 (·) with process noise Q k−1 .…”

Section: Gaussian Particle Filteringmentioning

confidence: 99%

Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models

Clark

et al. 2008

IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications

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The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, called the Particle PHD filter, requires clustering techniques to provide target state estimates which can lead to inaccurate estimates and is computationally expensive. The second algorithm, called the Gaussian Mixture PHD (GM-PHD) filter does not require clustering algorithms but is restricted to linear-Gaussian target dynamics, since it uses the Kalman filter to estimate the means and covariances of the Gaussians. This article provides a review of Gaussian filtering techniques for non-linear filtering and shows how these can be incorporated within the Gaussian mixture PHD filters. Finally, we show some simulated results of the different variants.

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Gaussian sum particle filtering

Cited by 433 publications

References 24 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Distributed ensemble Kalman filtering

Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models

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