Particle Filters for Random Set Models

22.05.2015

Englisch

9781489988843

Springer US

Nein

Branko Ristic

174

2013

Taschenbuch

Bayesian Estimation, Bernoulli Filter, Filtering Algorithms, Monte Carlo Statistical Method, Multi-target Filter, Particle Filters, Random-set Based Filters, Stochastic Filtering, information and communication, circuits

TJF - Elektronik UYS - Digitale Signalverarbeitung (DSP) UYA - Theoretische Informatik UYAM - Mathematik für Informatiker PBT - Wahrscheinlichkeitsrechnung und Statistik UYQ - Künstliche Intelligenz

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

235 mm

15.5 cm