Multitarget Bayes filtering via first-order multitarget moments
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Summary
Recursion Bayes filter equations for the probability hypothesis density are derived that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets and it is shown that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.
- Type
- article
- Published
- 2003-10-01
- Cited by
- 2,281
- References
- 41
- OpenAlex
- https://openalex.org/W2014787937
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:121330285
Keywords
Moment (physics), Kalman filter, Bayes' theorem, Filter (signal processing), Mathematics
References
- A Branching Particle-based Nonlinear Filter for Multi-target Tracking
- A Theoretical Foundation for the Stein-Winter "Probability Hypothesis Density (PHD)" Multitarget Tracking Approach
- Multitarget Moments and their Application to Multitarget Tracking
- Random sets in data fusion: formalism to new algorithms
- A microdensity approach to multitarget tracking
- Particle-systems implementation of the PHD multitarget-tracking filter
- Global posterior densities for sensor management
- Particle filtering algorithm for tracking multiple road-constrained targets
- Multisource multitarget filtering: a unified approach
- Multitarget Markov motion models
- On optimal filtering of multitarget tracking systems based on point processes observations
- Bayesian cluster detection and tracking using a generalized Cheeseman approach
- Jump-diffusion processes for the automated understanding of FLIR scenes
- Joint tracking and identification with robustness against unmodeled targets
- Locally Finite Random Sets: Foundations for Point Process Theory
- Bulk multitarget tracking using a first-order multitarget moment filter
- Multitarget detection and acquisition: a unified approach
- Practical implementation of joint multitarget probabilities
- Sampling from multitarget Bayesian posteriors for random sets via jump-diffusion processes
- A Bayesian approach to problems in stochastic estimation and control
Cited by
- Detection and tracking of multiple targets using wireless sensor networks - Detección y seguimiento de múltiples blancos en redes inalámbricas de sensores
- Méthodes conjointes de détection et suivi basé-modèle de cibles distribuées par filtrage non-linéaire dans les données lidar à balayage. (Joint detection and model-based tracking methods of extended targets in scanning laser rangefinder data using non-linear filtering techniques)
- Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking
- Loop detection and extended target tracking using laser data
- A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds
- Scalable Multisensor Multitarget Tracking Using the Marginalized δ-GLMB Density
- UAV Path and Sensor Planning Methods for Multiple Ground Target Search and Tracking - A Literature Survey
- Stochastic models and methods for multi-object tracking
- A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences
- Multistatic Tracking with the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker
- On line Bayesian tracking and detection of multiple objects
- Practical methods for Gaussian mixture filtering and smoothing
- Clutter Mitigation for Target Tracking
- Estimation, Decision and Applications to Target Tracking
- Tracking and Planning for Surveillance Applications
- On the Advantage of Wideband Data Acquisition for Passive Diver Detection
- A Unified Approach for Multi-Object Triangulation, Tracking and Camera Calibration
- Bayesian filtering for automotive applications
- Monte Carlo realisation of a distributed multi-object fusion algorithm
- Distributed fusion of multitarget densities and consensus PHD/CPHD filters
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