4.2.4 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