Beyond the Kalman Filter: Particle Filters for Tracking Applications

By B. Ristic, S. Arulampalam, Neil J. Gordon. 2004

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Citations in the corpus

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, methodology): “We make use of the Frank-Wolfe (FW) quadrature to approximate, in particular, mixtures of Gaussians which often arise in a particle ﬁltering context as the mixture over past particles in the distribution over the next state.”
Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, background): “online (sequentially), as each y tis received. For example, a tracking problem can be formulated as ﬁltering, where x tis the position of an object to be tracked, and y tis a noisy observation of x t(Ristic et al., 2004). As an inference problem, the starting point of ﬁltering is that the observatio”
A Numerical-Integration Perspective on Gaussian Filters (2006, methodology): “We also presented the Cramer‐Rao lower bound (CRLB) [26], [ 51 ] for the reference of performance comparison.”
Biips: Software for Bayesian Inference with Interacting Particle Systems (2014, background): “They have found numerous applications in signal ﬁltering and robotics (Thrun et al. 2001; Gustafsson et al. 2002; Vermaak et al. 2002; Vo et al. 2003; Ristic et al. 2004; Caron et al. 2007), systems biology (Golightly and Wilkinson 2006, 2011; Bouchard-Côté et al. 2012), economics and…”
Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction (2008, background): “The resampling and regularisation steps are explained in, for example, [12].”
Cell Population Tracking and Lineage Construction with Spatiotemporal Context (2007, background): “Amotion filter is the fundamental building block of many tracking systems (Ristic et al., 2004).”
Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models (2018, background): “The PF is one of the solutions to nonlinear problems by simulating enough particles [25].”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “This is analogous to the idea of using a measurement driven proposal in the particle filter, to guide particles towards important areas of the state space [53], [54].”
Particle gibbs with ancestor sampling (2014, background): “…many models for which the transition is implicitly given by a simulator (Gander and Stephens, 2007; Fearnhead et al., 2008; Golightly and Wilkinson, 2008; Murray et al., 2013) or degenerate models where the transition density does not even exist (Ristic et al., 2004; Gustafsson et al., 2002).”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, background): “The interested reader is referred to Ristic et al. (2004) for details of the algorithm; summaries in a marine ecological context are given in Losa et al. (2003) and Dowd (2006).”
Convolution Particle Filter for Parameter Estimation in General State-Space Models (2009, background): “advanced problems like multiple targets tracking and association, or maneuvering targets, one can consult [13, 15, 2, 19, 3].”
Assimilation of remote sensing into crop growth models: Current status and perspectives (2019, methodology): “A number of implementations of these filters are straightforward extensions of the basic MCMC algorithms (Dowd, 2006, 2007), while others are more directly based on importance sampling (van Leeuwen, 2009; Ristic et al., 2004; Arulampalam et al., 2002).”
SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, methodology): “The KF has been broadly applied to problems covering state and parameter estimation, signal processing, target tracking, fault detection and diagnosis, and even financial analysis [31,32].”
Unfreezing the robot: Navigation in dense, interacting crowds (2010, methodology): “To cope with the multimodality, we use an approximate inference technique based on importance sampling, a well understood approximate inference method for Bayesian statistics (for an introduction see [1], [4], [21], [24]; for a more detailed, up to date analysis of the method see [5], [13]).”