E-book
Particle Filters for Random Set Models [electronic resource] / by Branko Ristic.
Record details
- ISBN: 9781461463160
- Physical Description: XIV, 174 p. 52 illus., 41 illus. in color. online resource.
- Publisher: New York, NY : Springer New York : 2013.
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Electronic resources
| 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. | ||
| ^^ |
