Joint tracking and identification with robustness against unmodeled targets
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Summary
An application of a particle systems implementation of the probability hypothesis density (PHD) for joint tracking and identification of multiple aircraft, with the observations consisting of noisy position measurements and high range resolution radar (HRRRR) signatures.
- Type
- article
- Published
- 2003-08-25
- Cited by
- 36
- References
- 3
- OpenAlex
- https://openalex.org/W2012348964
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:128616119
Keywords
Clutter, Robustness (evolution), Computer science, Radar tracker, Radar
References
Cited by
- Stochastic models and methods for multi-object tracking
- Random Finite Sets for Robot Mapping and SLAM - New Concepts in Autonomous Robotic Map Representations
- CBMeMBer filters for nonstandard targets, I: Extended targets
- Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter
- CBMeMBer filters for nonstandard targets, II: Unresolved targets
- Birth Density Modeling in Multi-target Tracking Using the Gaussian Mixture PHD Filter
- Nonlinear Filtering Algorithms for Multitarget Tracking
- Particle-systems implementation of the PHD multitarget-tracking filter
- An Improved Joint Target Tracking and Classification Algorithm Based on Data Fusion
- Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter
- Laser and Radar Based Robotic Perception
- Multitarget Bayes filtering via first-order multitarget moments
- PHD filters of higher order in target number
- Joint Detection, Tracking, and Classification of Multiple Targets in Clutter using the PHD Filter
- Techniques for birth-particle placement in the probability hypothesis density particle filter applied to passive radar
- Multi-Sensor Joint Detection and Tracking with the Bernoulli Filter
- Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
- Random finite sets in Multi-object filtering
- Multiple extended objects tracking with object-local occupancy grid maps
- An improved PHD filter based on variational Bayesian method for multi-target tracking
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