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Particle Filters for Random Set Models - Branko Ristic

Particle Filters for Random Set Models

By: Branko Ristic

Paperback | 22 May 2015

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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 Implementation

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

Industry Reviews

From the book reviews:

"The book realizes a happy union between theory and practice. Of high interest are the Algorithms for which their pseudo-codes are presented. We think we are faced with an excellent book that will have a great success and audience between those interested for new approaches in filtering theory." (Dumitru Stanomir, zbMATH 1306.93002, 2015)

Other Editions and Formats

Hardcover

Published: 15th April 2013

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