The marginalized particle filter – 
analysis, applications and generalizations

Schön, Thomas B.; Karlsson, Rickard; Gustafsson, Fredrik

doi:10.1051/proc:071908

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…An analytic approximation tries to derive a closed-form expression. A marginalized PF is a combination of the two (Schön et al 2007(Schön et al , Özkana et al 2013. The VA is a well-developed analytic one.…”

Section: Appendix a Derivations Of The Vasb Algorithmmentioning

confidence: 99%

Time-varying forecasts by variational approximation of sequential Bayesian inference

Ling

Stone

2015

Quantitative Finance

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Methods developed for making time-varying forecasts in economic and financial analysis include (a) equal-weighted moving-window (or rolling) regression, (b) time-weighted (e.g. exponentially weighted) regression, (c) the Kalman filter (KF) and (d) adaptive Kalman filters. This paper developed a new method based on variational approximation of sequential Bayesian inference (VASB). Concepts and notions of the sequential Bayesian analysis and the variational approximation of an intractable posterior are simple and straightforward. Our VASB algorithm is not complicated and is easy to code. For a regression on multiple time-series, the regression coefficients, standard errors, prediction and residual error are time-varying and are estimated jointly at every time step. For a single time-series (e.g. price returns of an asset), its mean and variance are time-varying and are predicted jointly at every time step. The VASB algorithm performs better than the rolling and time-weighted statistics or regressions and the KF in terms of higher predictive power and stronger robustness. Derivations of the VASB algorithm are presented in the appendices.

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Section: Appendix a Derivations Of The Vasb Algorithmmentioning

confidence: 99%

Time-varying forecasts by variational approximation of sequential Bayesian inference

Ling

Stone

2015

Quantitative Finance

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“…This is accomplished by considering the joint distribution of the state of the local and the collaborating nodes, conditioned on the relative range measurement, p x 1:k , x c k z R 1:k . The problem is further simplified by utilizing the Rao-Blackwellized particle filter (RBPF) formulation [49,50], in which the joint distribution can be factored into a conditionally linear component and a nonlinear component:…”

Section: Distributed Relative-range Measurement Updatementioning

confidence: 99%

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

Sakr

Masiero

El‐Sheimy

2019

Sensors

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This paper presents LocSpeck, a collaborative and distributed indoor positioning system for dynamic nodes connected using an ad-hoc network, based on inter-node relative range measurements and Wi-Fi fingerprinting. The proposed system operates using peer-to-peer range measurements and does not need ultra-wideband (UWB) fixed anchor, nor it needs a predefined network topology. The nodes could be asymmetric in terms of the available sensors onboard, the computational resources, and the power capacity. This asymmetry adversely affects the positioning performance of the weaker nodes. Collaboration between different nodes is achieved through a distributed estimator without the need of a single centralized computing element. The ranging measurement component of the system is based on the DW1000 UWB transceiver chip from Decawave, which is attached to a set of smartphones equipped with asymmetric sensors. The distributed positioning filter fuses, locally on each node, the relative range measurements, the reading from the internal sensors, and the Wi-Fi received signal strength indicator (RSSI) readings to obtain an estimate of the position of each node. The described system does not depend on fixed UWB anchors and supports online addition and removal of nodes and dynamic node role assignment, either as an anchor or as a rover. The performance of the system is evaluated by real-world test scenarios using a set of four smartphones navigating an indoor environment on foot. The performance is compared to that of a commercial UWB-based system. The results presented in this paper show that weak mobile nodes, in terms of available positioning sensors, can benefit from collaboration with other nearby nodes.Sensors 2020, 20, 78 2 of 28 using relative measurements can augment the stand-alone observations of each node and improve the positioning accuracy of the ensemble [2,3]. The use of relative range measurements introduces an additional constraint to the position estimation filter, which can improve the positioning accuracy of the collaborating nodes [4]. Ultra-wideband (UWB) ranging devices are more immune to multipath errors because it can distinguish between different events with a precise temporal resolution, thanks to its large channel bandwidth. This makes the UWB-based devices capable of achieving centimeter-level ranging accuracy in ideal operating conditions [5].The objective of this paper is to present LocSpeck, a collaborative and distributed positioning system targeting smartphones and handheld applications, which uses UWB-based relative range measurements, along with Wi-Fi fingerprinting and inertial sensors. The system is evaluated experimentally, and the results are compared to the performance of Pozyx, a commercial UWB-based positioning system. The rest of this section will provide a brief overview of different ranging and positioning techniques used in UWB-based systems, and then it will discuss the different network architectures used for range-based positioning applications. UWB-Based RangingSensors 2020, 20,...

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“…In [23] the authors compare the number of particles needed to obtain equivalent performance using different partitions of the state space in particle filter states and Kalman filter states. The RBPF method has also enabled efficient implementation of recursive Bayesian estimation in many applications, ranging between automotive, aircraft, UAV and naval applications [11,[24][25][26][27][28][29][30].…”

Section: Recursive Bayesian Estimationmentioning

confidence: 99%

The Rao-Blackwellized Particle Filter: A Filter Bank Implementation

Hendeby

Karlsson

Gustafsson

2010

EURASIP J. Adv. Signal Process.

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For computational efficiency, it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure, which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF). This contribution suggests an alternative formulation of this wellknown result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning.

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The marginalized particle filter – analysis, applications and generalizations

Cited by 5 publications

References 31 publications

Time-varying forecasts by variational approximation of sequential Bayesian inference

Time-varying forecasts by variational approximation of sequential Bayesian inference

LocSpeck: A Collaborative and Distributed Positioning System for Asymmetric Nodes Based on UWB Ad-Hoc Network and Wi-Fi Fingerprinting

The Rao-Blackwellized Particle Filter: A Filter Bank Implementation

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