Belief Condensation Filtering

Mazuelas, Santiago; Shen, Yuan; Win, Moe Z.

doi:10.1109/tsp.2013.2261991

Cited by 43 publications

(39 citation statements)

References 50 publications

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“…The use of the last rules and of those expressed by Eqs. (2) and (3) can be exemplified by taking into consideration again the function f (x 1 , x 2 , x 3 , x 4 ) (1) (which is assumed now to represent the joint pdf of four continuous random variables) and showing how, thanks to these rules, the marginal pdf f (x 3 ) can be evaluated in a step-by-step fashion. If the messages m 1 (x 1 ) = f 1 (x 1 ), m 0 (x 2 ) = 1 and…”

Section: A Factor Graphs and The Sum-product Algorithmmentioning

confidence: 99%

Multiple Bayesian Filtering as Message Passing

Vitetta

Viesti

Sirignano

et al. 2020

IEEE Trans. Signal Process.

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In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering. particle filter are run over partially overlapped state vectors. In both cases, however, two heterogeneous filtering methods are combined in a way that the resulting overall algorithm is forward only and, within each of its recursions, both methods are executed only once. Another class of solutions, known as multiple particle filtering (MPF), is based on the idea of partitioning the state vector into multiple substates and running multiple particle filters in parallel, one on each subspace [9], [12]- [15]. The resulting network of particle filters requires the mutual exchange of statistical information (in the form of estimates/predictions of the tracked substates or parametric distributions), so that, within each filter, the unknown portion of the state vector can be integrated out in both weight computation and particle propagation. In principle, MPF can be employed only when the selected substates are separable in the state equation, even if approximate solutions can be devised to circumvent this problem [15]. Moreover, the technical literature about MPF has raised three interesting technical issues that have received limited attention until now. The first issue refers to the possibility of coupling an extended Kalman filter with each particle filter of the network; the former filter should provide the latter one with the statistical information required for integrating out the unknown portion of the state vector (see [14, Par. 3.2]). The second one concerns the use of filters having partially overlapped substates (see [13, Sec.1]). The third (and final) issue, instead, concerns the iterative exchange of statistical information among the interconnected filters of the network. Some work related to the first issue can be found in [16], where the application of MPF to target tracking in a cognitive radar network has been investigated. In this case, however, the proposed solution is based on Rao-Blackwellisation; for this reason, each particle filter of the network is not coupled with a single extended Kalman filter, but with a bank of Kalman filters. The second issue has not been investigated at all, whereas limited attention has been paid to the third one; in fact, the last problem has been investigated only in [12], where a specific iterative method ba...

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Section: A Factor Graphs and The Sum-product Algorithmmentioning

confidence: 99%

Multiple Bayesian Filtering as Message Passing

Vitetta

Viesti

Sirignano

et al. 2020

IEEE Trans. Signal Process.

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“…In terms of the Euler angle vector , the vectorization of (16b) is (17) where and are defined in Appendix C. For the 2D scenario, is a 2 2 matrix given by (16a) after removing the middle column and the middle row. The vectorized form (17) remains valid with different definitions of , and .…”

Section: B Step-2: Refinementmentioning

confidence: 99%

Accurate Localization of a Rigid Body Using Multiple Sensors and Landmarks

Chen

2015

IEEE Trans. Signal Process.

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This paper develops estimators for locating a rigid body using the time measurements, and the Doppler as well if it is moving, between the sensors in the rigid body and a few landmarks outside. The challenge of rigid body localization is that in addition to the position, we are also interested in obtaining the rotation parameters of the rigid body that must belong to the special orthogonal group. The proposed estimators are non-iterative and have two steps: preliminary and refinement. The preliminary step provides a coarse estimate and the refinement step improves the first step estimate to yield an accurate solution. When the rigid body is stationary, we are able to locate the body with accuracy higher than the solutions of comparable complexity found in the literature. When the rigid body is moving, we develop an estimator that contains the additional unknowns of angular and translational velocities. Simulations show that the proposed estimators, in both stationary and moving cases, can approach the Cramer-Rao Lower Bound performance under Gaussian noise over the small error region.

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“…The complexity of both extended and unscented transformations is on the order of the cube of the dimension of the state, which cannot compromise their real-time operation [61]. Other approaches, such as particle filters or Gaussian mixture filters, are discarded since they suffer from the curse of dimensionality induced by the dimension of the state vector [61,62]. (The number of filtered path-loss exponents, and consequently the dimension of the state, grows with the number of anchors used for ranging.)…”

Section: Real-time Filtering For Localizationmentioning

confidence: 99%

Unified Fingerprinting/Ranging Localization in Harsh Environments

Prieto

Paz

Villarrubia

et al. 2015

International Journal of Distributed Sensor Networks

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Context-awareness in wireless sensor networks (WSNs) relies mainly on the position of objects and humans. The provision of this positional information becomes challenging in the harsh environmental conditions where WSNs are commonly deployed. With an antagonistic philosophy of design, fingerprinting and ranging have emerged as the key technologies underpinning wireless localization in harsh environments. Fingerprinting primarily focuses on accurate estimation at the expense of exhaustive calibration. Ranging mainly pursues an easy-to-deploy solution at the expense of moderate performance. In this paper, we present a resilient framework for sustained localization based on accurate fingerprinting in critical areas and light ranging in noncritical spaces. Such framework is conceived from the Bayesian perspective that facilitates the specification of recursive algorithms for real-time operation. In comparison to conventional implementations, we assessed the proposed framework in an indoor scenario with measurements gathered by commercial devices. The presented techniques noticeably outperform current approaches, enabling a flexible adaptation to the fluctuating conditions of harsh environments.

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Belief Condensation Filtering

Cited by 43 publications

References 50 publications

Multiple Bayesian Filtering as Message Passing

Multiple Bayesian Filtering as Message Passing

Accurate Localization of a Rigid Body Using Multiple Sensors and Landmarks

Unified Fingerprinting/Ranging Localization in Harsh Environments

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