Branko Ristic is at the Defence Science and Technology Organisation, Australia

Defence Science and Technology Organisation, Australia

Presents a hands-on engineering approach to filtering algorithms and their implementation

Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications

Includes sensor control for particle filters

Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters

3.3.2 Classification results
References
4 Multi-object particle filters
4.1 Bernoulli particle filters
4.1.1 Standard Bernoulli particle filters
4.1.2 Bernoulli box-particle filter
4.2 PHD/CPDH particle filters with adaptive birth intensity
4.2.1 Extension of the PHD filter
4.2.2 Extension of the CPHD filter
4.2.3 Implementation4.2.4 A numerical study
4.2.5 State estimation from PHD/CPHD particle filters
4.3 Particle filter approximation of the exact multi-object filter
References
5 Sensor control for random set based particle filters
5.1 Bernoulli particle filter with sensor control
5.1.1 The reward function
5.1.2 Bearings only tracking in clutter with observer control
5.1.3 Target Tracking via Multi-Static Doppler Shifts
5.2 Sensor control for PHD/CPHD particle filters
5.2.1 The reward function
5.2.2 A numerical study
5.3 Sensor control for the multi-target state particle filter
5.3.1 Particle approximation of the reward function
5.3.2 A numerical study
References
6 Multi-target tracking
6.1 OSPA-T: A performance metric for multi-target tracking
6.1.1 The problem and its conceptual solution
6.1.2 The base distance and labeling of estimated tracks
6.1.3 Numerical examples
6.2 Trackers based on random set filters
6.2.1 Multi-target trackers based on the Bernoulli PF
6.2.2 Multi-target trackers based on the PHD particle filter
6.2.3 Error performance comparison using the OSPA-T error
6.3 Application: Pedestrian tracking
6.3.1 Video dataset and detections
6.3.2 Description of Algorithms
6.3.3 Numerical results
References
7 Advanced topics
7.1 Bernoulli filter for extended target tracking
7.1.1 Mathematical models
7.1.2 Equations of the Bernoulli filter for an extended target
7.1.3 Numerical Implementation
7.1.4 Simulation results
7.1.5 Application to a surveillance video
7.2 Calibration of tracking systems
7.2.1 Background and problem formulation
7.2.2 The proposed calibration algorithm
7.2.3 Importance sampling with progressive correction
7.2.4 Application to sensor bias estimation
References
Index