Bootstrap Particle Filtering

Candy, James V.

doi:10.1109/msp.2007.4286566

Cited by 92 publications

(55 citation statements)

References 16 publications

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“…Particle filtering, also known as the sequential Monte Carlo method, is a successful technique to recursively estimate hidden states of nonlinear non-Gaussian dynamical systems [34]. The mathematical framework of the particle filtering is not detailed here, but for a good introduction, the interested reader is referred to appropriate papers such as [35] and [36]. To briefly summarize, one particle is composed of a state value, i.e., a hypothesis, and an associated weight, i.e., the probability that this hypothesis is true regarding the observation.…”

Section: Optimal Intersensor Distancementioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments.

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Section: Optimal Intersensor Distancementioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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“…where I(X; Y |Z) is the conditional mutual information rate between X and Y given Z, the numerator inside the logarithm in (6) is obtained by (4), and the denominator inside the logarithm in (6) is obtained by chain rule. By the Shannon-McMillan-Breiman theorem, one can generate a joint sequence (s n 0 , y n 1 ) according to the actual joint state transition probability and measurement probability…”

Section: Evaluation Of the Information Rate By Exact Bayesian Tramentioning

confidence: 99%

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Barletta

Magarini

Pecorino

et al. 2014

IEEE Trans. Inform. Theory

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Abstract-Starting from the definition of mutual information, one promptly realizes that the probabilities inferred by Bayesian tracking can be used to compute the Shannon information between the state and the measurement of a dynamic system. In the Gaussian and linear case, the information rate can be evaluated from the probabilities computed by the Kalman filter. When the probability distributions inferred by Bayesian tracking are non tractable, one is forced to resort to approximated inference, which gives only an approximation to the wanted probabilities. We propose upper and lower bounds to the information rate between the hidden state and the measurement based on approximated inference. Application of these bounds to multiplicative communication channels is discussed, and experimental results for the discrete-time phase noise channel and for the GaussMarkov fading channel are presented.

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“…Our main aim is to introduce a fast and reliable PF on a GPU. We restrict ourselves to particle filters which use sequential importance resampling (SIR) [9]. The PF algorithm (as described in the next section in detail) has a high running time due to the resampling step -according to the complete cumulative distribution; therefore, an adequate parallel implementation would fetch a remarkable speed-up.…”

Section: Introductionmentioning

confidence: 99%

Fast, parallel implementation of particle filtering on the GPU architecture

Gelencsér-Horváth

Tornai

Horváth

et al. 2013

EURASIP J. Adv. Signal Process.

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In this paper, we introduce a modified cellular particle filter (CPF) which we mapped on a graphics processing unit (GPU) architecture. We developed this filter adaptation using a state-of-the art CPF technique. Mapping this filter realization on a highly parallel architecture entailed a shift in the logical representation of the particles. In this process, the original two-dimensional organization is reordered as a one-dimensional ring topology. We proposed a proof-of-concept measurement on two models with an NVIDIA Fermi architecture GPU. This design achieved a 411-μs kernel time per state and a 77-ms global running time for all states for 16,384 particles with a 256 neighbourhood size on a sequence of 24 states for a bearing-only tracking model. For a commonly used benchmark model at the same configuration, we achieved a 266-μs kernel time per state and a 124-ms global running time for all 100 states. Kernel time includes random number generation on the GPU with curand. These results attest to the effective and fast use of the particle filter in high-dimensional, real-time applications.

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Bootstrap Particle Filtering

Cited by 92 publications

References 16 publications

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Upper and Lower Bounds to the Information Rate Transferred Through First-Order Markov Channels With Free-Running Continuous State

Fast, parallel implementation of particle filtering on the GPU architecture

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