Particle Filters for Random Set Models /
| Autor principal: | |
|---|---|
| Autor Corporativo: | |
| Formato: | eBook |
| Lenguaje: | English |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
|
| Edición: | 1st ed. 2013. |
| Materias: |
Tabla de Contenidos:
- Introduction
- References
- Background
- A brief review of particle filters
- Online sensor control
- Non-standard measurements
- Imprecise measurements
- Imprecise measurement function
- Uncertain implication rules
- Particle filter implementation
- Applications
- Multiple objects and imperfect detection
- Random finite sets
- Multi-object stochastic filtering
- OSPA metric
- Specialized multi-object filters
- Bernoulli filter
- PHD and CPHD filter
- References
- Applications involving non-standard measurements
- Estimation using imprecise measurement models
- Localization using the received signal strength
- Prediction of an epidemic using syndromic data
- Summary
- Fusion of spatially referring natural language statements
- Language, space and modelling
- An illustrative example
- Classification using imprecise likelihoods
- Modelling
- Classification results
- References
- object particle filters
- Bernoulli particle filters
- Standard Bernoulli particle filters
- Bernoulli box-particle filter
- PHD/CPDH particle filters with adaptive birth intensity
- Extension of the PHD filter
- Extension of the CPHD filter
- Implementation
- A numerical study
- State estimation from PHD/CPHD particle filters
- Particle filter approximation of the exact multi-object filter
- References
- Sensor control for random set based particle filters
- Bernoulli particle filter with sensor control
- The reward function
- Bearings only tracking in clutter with observer control
- Target Tracking via Multi-Static Doppler Shifts
- Sensor control for PHD/CPHD particle filters
- The reward function
- A numerical study
- Sensor control for the multi-target state particle filter
- Particle approximation of the reward function
- A numerical study
- References
- Multi-target tracking
- OSPA-T: A performance metric for multi-target tracking
- The problem and its conceptual solution
- The base distance and labeling of estimated tracks
- Numerical examples
- Trackers based on random set filters
- Multi-target trackers based on the Bernoulli PF
- Multi-target trackers based on the PHD particle filter
- Error performance comparison using the OSPA-T error
- Application: Pedestrian tracking
- Video dataset and detections
- Description of Algorithms
- Numerical results
- References
- Advanced topics
- Filter for extended target tracking
- Mathematical models
- Equations of the Bernoulli filter for an extended target
- Numerical Implementation
- Simulation results
- Application to a surveillance video
- Calibration of tracking systems
- Background and problem formulation
- The proposed calibration algorithm
- Importance sampling with progressive correction
- Application to sensor bias estimation
- References
- Index.