The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
IEEE Transactions on Signal Processing
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
Brief paper: Sensor control for multi-object state-space estimation using random finite sets
Automatica (Journal of IFAC)
Joint detection and estimation of multiple objects from image observations
IEEE Transactions on Signal Processing
Ants with three primary colors for track initiation
Expert Systems with Applications: An International Journal
PHD filter based track-before-detect for MIMO radars
Signal Processing
Extensions of the SMC-PHD filters for jump Markov systems
Signal Processing
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
A novel track maintenance algorithm for PHD/CPHD filter
Signal Processing
Visual tracking of numerous targets via multi-Bernoulli filtering of image data
Pattern Recognition
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
High-speed Sigma-gating SMC-PHD filter
Signal Processing
Hi-index | 0.02 |
The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.