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## Wireless sensor network Particle filters (2013)

### Citations

3854 |
A New Approach to Linear Filtering and Prediction Problems
- Kalman
- 1960
(Show Context)
Citation Context ...osterior mean of xt , i.e., xMMSEt R xt pðxt jy1:tÞ dxt . If the system of Eq. (1) is linear and Gaussian, then pðxt jy1:tÞ is Gaussian and can be obtained exactly using the Kalman filter algorithm =-=[23]-=-. If the state space is discrete and finite, exact solutions can also be computed [31]. However if any of the pdf's in (1) is non-Gaussian, or the system is nonlinear, we have to resort to suboptimal ... |

1733 |
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
- Gordon, Salmond, et al.
- 1993
(Show Context)
Citation Context ...ntation of the PF is obtained when we choose πtðx0:tÞ pðx0:tÞ pðx0Þ∏tk 1pðxkjxk#1Þ and perform resampling steps [17] at every time step. The resulting algorithm is often called bootstrap filter =-=[19]-=- or sequential importance resampling (SIR) algorithm [17]. We outline it in Table 1 and will henceforth refer to it as a centralized particle filter (CPF). The resampling step randomly eliminates samp... |

1488 | Wireless sensor networks for habitat monitoring
- Mainwaring, Polastre, et al.
- 2002
(Show Context)
Citation Context ...al monitoring (tracking of weather patterns and pollutants) [32], monitoring in domestic situations (such as in care for the elderly) [22], and biology (tracking of populations or individual animals) =-=[27]-=-. Distributed applications of tracking are particularly interesting in situations where high powered centralized hardware cannot be used. For example, in deployments where computational infrastructure... |

1051 | On sequential Monte Carlo sampling methods for Bayesian filtering
- Doucet, Godsill, et al.
- 2000
(Show Context)
Citation Context ... state based on a random point-mass (or “particle”) representation of the probability measure with density pðxt jy1:tÞ [16,3]. It is often convenient to derive PFs as instances of the SIS methodology =-=[17]-=-. Consider the joint posterior density pðx0:t jy1:tÞ. Bayes' theorem, together with the Markov property of the state and the conditional independence of the observations, yields the recursive relation... |

735 |
Estimation with Applications to Tracking and Navigation
- Bar-Shalom, Li, et al.
- 2001
(Show Context)
Citation Context ...gned to keep the target motion within the limits of the region A (also described below). The indicator at determines the kind of motion of the target. If at0, then ftð-;0Þ yields a constant-velocity =-=[4]-=- model. If, on the other hand, at 1, then fð-;1Þ produces a sharp turn by generating a velocity vector independent of the velocity at time t#1, specifically: fðxt#1;0Þ 1 0 Ts 0 0 1 0 Ts 0 0 1 0 0 ... |

279 | Nonparametric belief propagation
- Sudderth, Ihler, et al.
- 2003
(Show Context)
Citation Context ...ations to the centralized PF whose convergence cannot be guaranteed [26]. The use of the non-parametric loopy belief propagation (NPBP) algorithm has also been suggested for localization and tracking =-=[33]-=- in the same context as particle filtering. This algorithm is an extension of the belief propagation algorithm to continuous variables and it uses a graph to represent the decomposition of the joint p... |

267 | An improved particle filter for non-linear problems
- Carpenter, Clifford, et al.
- 1999
(Show Context)
Citation Context ...nd replicates samples with high importance weights in order to avoid the degeneracy of the importance weights over time [17,31]. Here, we apply multinomial resampling, but several other choices exist =-=[8,17,15,3]-=-. We also assume that resampling is carried out at every time step, but applying the proposed methods with periodic or random resampling times is straightforward. Bayesian estimators of xt can be easi... |

184 | Distributed Kalman filtering for sensor networks, - Olfati-Saber - 2007 |

129 | Comparision of resampling schemes for particle filtering
- Douc, Cappe
- 2005
(Show Context)
Citation Context ...nd replicates samples with high importance weights in order to avoid the degeneracy of the importance weights over time [17,31]. Here, we apply multinomial resampling, but several other choices exist =-=[8,17,15,3]-=-. We also assume that resampling is carried out at every time step, but applying the proposed methods with periodic or random resampling times is straightforward. Bayesian estimators of xt can be easi... |

109 | Distributed particle filters for sensor networks
- Coates
(Show Context)
Citation Context ...). Signal Processing 98 (2014) 121–134 Particle filtering for target tracking in WSNs has already attracted some attention (see, for example, [5,1,2]), including a body of work in distributed methods =-=[10,21,7]-=-. Its relation with agent networks has also been explored in [20]. In [7], a fully decentralized particle filtering algorithm for cooperative blind equalization is introduced. The technique is proper,... |

109 |
Particle Filtering,
- Djuric, Kotecha, et al.
- 2003
(Show Context)
Citation Context ...is algorithm. 1.2. Distributed resampling with non-proportional allocation (DRNA) Particle filtering algorithms involve three basic steps: generation of samples, computation of weights and resampling =-=[12]-=-. While it is straightforward to parallelize the first two steps, resampling requires the joint processing of all the samples in the filter and so becomes a computational bottleneck. The distributed r... |

77 | Resampling algorithms and architectures for distributed particle filters,
- Bolic, Djuric, et al.
- 2005
(Show Context)
Citation Context ...teps, resampling requires the joint processing of all the samples in the filter and so becomes a computational bottleneck. The distributed resampling with non-proportional allocation (DRNA) algorithm =-=[6]-=- (see also [28] for some further analysis) addresses the parallelization of the resampling step to remove this bottleneck. The DRNA algorithm was devised to speed up the computations in particle filte... |

74 | On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods.
- Lee, Yau, et al.
- 2010
(Show Context)
Citation Context ...iltering. The basic assumption in [6] is the availability of a set of processors interconnected by a high-speed network, in the manner of stateof-the-art graphical processing unit (GPU) based systems =-=[25]-=-. Such network is intended to guarantee that all processors in the system have access to the full set of observed data. In a typical WSN, the communications among nodes are subject to various constrai... |

72 |
Fundamentals of Stochastic Filtering.
- Bain, Crisan
- 2000
(Show Context)
Citation Context ...d” specifically with regard to processing, meaning that the computational tasks are divided among a set of low-power devices in the WSN. 1.1. Distributed particle filters Stochastic filtering methods =-=[3]-=- are obvious candidates for tracking applications and so they have been researched by many authors in the context of WSNs [29,14,11]. Such work includes, e.g., networks of interacting Kalman filters [... |

60 | SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations,”
- Ribeiro, Giannakis, et al.
- 2006
(Show Context)
Citation Context ...] are obvious candidates for tracking applications and so they have been researched by many authors in the context of WSNs [29,14,11]. Such work includes, e.g., networks of interacting Kalman filters =-=[30]-=-, although in this case the emphasis is on the minimization of the communications among nodes, rather than the sharing of the computational load. Contents lists available at ScienceDirect journal home... |

28 |
Target Tracking by Particle Filtering in Binary Sensor Networks,”
- Djuric, Vermula, et al.
- 2008
(Show Context)
Citation Context ...in the WSN. 1.1. Distributed particle filters Stochastic filtering methods [3] are obvious candidates for tracking applications and so they have been researched by many authors in the context of WSNs =-=[29,14,11]-=-. Such work includes, e.g., networks of interacting Kalman filters [30], although in this case the emphasis is on the minimization of the communications among nodes, rather than the sharing of the com... |

24 | First experiences using wireless sensor networks for noise pollution monitoring. In:
- Santini, Ostermaier, et al.
- 2008
(Show Context)
Citation Context ...an increasingly attractive option for a growing number of tracking applications. Examples include security and surveillance [5], environmental monitoring (tracking of weather patterns and pollutants) =-=[32]-=-, monitoring in domestic situations (such as in care for the elderly) [22], and biology (tracking of populations or individual animals) [27]. Distributed applications of tracking are particularly inte... |

17 |
P.M.: Distributed Particle Filtering in Agent Networks: A Survey, Classification, and Comparison.
- Hlinka, Hlawatsch, et al.
- 2013
(Show Context)
Citation Context ...racking in WSNs has already attracted some attention (see, for example, [5,1,2]), including a body of work in distributed methods [10,21,7]. Its relation with agent networks has also been explored in =-=[20]-=-. In [7], a fully decentralized particle filtering algorithm for cooperative blind equalization is introduced. The technique is proper, in the sense that it does not make any approximations in the com... |

16 |
Analysis of parallelizable resampling algorithms for particle filtering
- Mı́guez
(Show Context)
Citation Context ...ng requires the joint processing of all the samples in the filter and so becomes a computational bottleneck. The distributed resampling with non-proportional allocation (DRNA) algorithm [6] (see also =-=[28]-=- for some further analysis) addresses the parallelization of the resampling step to remove this bottleneck. The DRNA algorithm was devised to speed up the computations in particle filtering. The basic... |

11 |
An elderly health care system using wireless sensor networks at home,” in Sensor Technologies and Applications,
- Huo, Xu, et al.
- 2009
(Show Context)
Citation Context ...tions. Examples include security and surveillance [5], environmental monitoring (tracking of weather patterns and pollutants) [32], monitoring in domestic situations (such as in care for the elderly) =-=[22]-=-, and biology (tracking of populations or individual animals) [27]. Distributed applications of tracking are particularly interesting in situations where high powered centralized hardware cannot be us... |

11 | Distributed Particle Filter for Target Tracking
- Liu, So, et al.
(Show Context)
Citation Context ...easurements to track an additional moving target node. There are a few exceptions of actually distributed PFs, but they are approximations to the centralized PF whose convergence cannot be guaranteed =-=[26]-=-. The use of the non-parametric loopy belief propagation (NPBP) algorithm has also been suggested for localization and tracking [33] in the same context as particle filtering. This algorithm is an ext... |

9 | Cooperative Target Tracking in a Distributed Autonomous Sensor Network
- Eickstedt, Benjamin
- 2006
(Show Context)
Citation Context ...ems in the literature often refer to WSNs as being “distributed”, even when processing is centralized, because they are merely referring to the physically distributed nature of WSNs. See, for example =-=[9,18]-=-. In this paper we refer to “distributed” specifically with regard to processing, meaning that the computational tasks are divided among a set of low-power devices in the WSN. 1.1. Distributed particl... |

8 |
Distributed gaussian particle filtering using likelihood consensus
- Hlinka, Sluciak, et al.
- 2011
(Show Context)
Citation Context ...). Signal Processing 98 (2014) 121–134 Particle filtering for target tracking in WSNs has already attracted some attention (see, for example, [5,1,2]), including a body of work in distributed methods =-=[10,21,7]-=-. Its relation with agent networks has also been explored in [20]. In [7], a fully decentralized particle filtering algorithm for cooperative blind equalization is introduced. The technique is proper,... |

4 | Detection and tracking using wireless sensor networks
- Ahmed, Dong, et al.
(Show Context)
Citation Context ...ponding author. E-mail address: jesse@tsc.uc3m.es (J. Read). Signal Processing 98 (2014) 121–134 Particle filtering for target tracking in WSNs has already attracted some attention (see, for example, =-=[5,1,2]-=-), including a body of work in distributed methods [10,21,7]. Its relation with agent networks has also been explored in [20]. In [7], a fully decentralized particle filtering algorithm for cooperativ... |

3 |
Fernández-Prades, Bayesian filtering for indoor localization and tracking in wireless sensor networks
- Dhital, Closas, et al.
(Show Context)
Citation Context ...in the WSN. 1.1. Distributed particle filters Stochastic filtering methods [3] are obvious candidates for tracking applications and so they have been researched by many authors in the context of WSNs =-=[29,14,11]-=-. Such work includes, e.g., networks of interacting Kalman filters [30], although in this case the emphasis is on the minimization of the communications among nodes, rather than the sharing of the com... |

2 |
A multimodel particle filtering algorithm for indoor tracking of mobile terminals using RSS data
- Achutegui, Martino, et al.
(Show Context)
Citation Context ...ponding author. E-mail address: jesse@tsc.uc3m.es (J. Read). Signal Processing 98 (2014) 121–134 Particle filtering for target tracking in WSNs has already attracted some attention (see, for example, =-=[5,1,2]-=-), including a body of work in distributed methods [10,21,7]. Its relation with agent networks has also been explored in [20]. In [7], a fully decentralized particle filtering algorithm for cooperativ... |

2 |
Salil Kanhere, Branko Ristic, Travis Bessell, Mark Rutten, Sanjay Jha. Wireless sensor networks for battlefield surveillance, in: Land Warfare Conference
- Bokareva, Hu
- 2006
(Show Context)
Citation Context ...ble hardware, the deployment of wireless sensor networks (WSNs) is becoming an increasingly attractive option for a growing number of tracking applications. Examples include security and surveillance =-=[5]-=-, environmental monitoring (tracking of weather patterns and pollutants) [32], monitoring in domestic situations (such as in care for the elderly) [22], and biology (tracking of populations or individ... |

2 | Cooperative bling equalization of frequency-selective channels in sensor networks using decentralized particle filtering - Bordin, Bruno |

2 |
Xiaoming Hu, Distributed sensor network for target tracking
- Cheng, Ghosh
- 2006
(Show Context)
Citation Context ...ems in the literature often refer to WSNs as being “distributed”, even when processing is centralized, because they are merely referring to the physically distributed nature of WSNs. See, for example =-=[9,18]-=-. In this paper we refer to “distributed” specifically with regard to processing, meaning that the computational tasks are divided among a set of low-power devices in the WSN. 1.1. Distributed particl... |