Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction to object tracking
- 2 Filtering theory and non-maneuvering object tracking
- 3 Maneuvering object tracking
- 4 Single-object tracking in clutter
- 5 Single- and multiple-object tracking in clutter: object-existence-based approach
- 6 Multiple-object tracking in clutter: random-set-based approach
- 7 Bayesian smoothing algorithms for object tracking
- 8 Object tracking with time-delayed, out-of-sequence measurements
- 9 Practical object tracking
- Appendix A Mathematical and statistical preliminaries
- Appendix B Finite set statistics (FISST)
- Appendix C Pseudo-functions in object tracking
- References
- Index
7 - Bayesian smoothing algorithms for object tracking
Published online by Cambridge University Press: 07 September 2011
- Frontmatter
- Contents
- Preface
- 1 Introduction to object tracking
- 2 Filtering theory and non-maneuvering object tracking
- 3 Maneuvering object tracking
- 4 Single-object tracking in clutter
- 5 Single- and multiple-object tracking in clutter: object-existence-based approach
- 6 Multiple-object tracking in clutter: random-set-based approach
- 7 Bayesian smoothing algorithms for object tracking
- 8 Object tracking with time-delayed, out-of-sequence measurements
- 9 Practical object tracking
- Appendix A Mathematical and statistical preliminaries
- Appendix B Finite set statistics (FISST)
- Appendix C Pseudo-functions in object tracking
- References
- Index
Summary
Estimation of an object state at a particular time based on measurements collected beyond that time is generally termed as smoothing or retrodiction. Smoothing improves the estimates compared to the ones obtained by filters owning to the use of more observations (or information). This comes at the cost of a certain time delay. However, these improvements are highly effective in applications like “situation awareness” or “threat assessment.” These higher level applications improve operator efficiency if a more accurate picture of the actual field scenario is provided to them, even if it is with a time delay. For these applications, besides object state, parameters representing the overall scenario, like number of targets, their initiation/termination instants and locations, may prove to be very useful ones. A smoothing algorithm can result in a better estimation of the overall situational picture and thus help increase the effectiveness of the critical applications like situation/ threat awareness. This chapter will introduce the Bayesian formulation of smoothing and derive the established smoothing algorithms under different tracking scenarios: non-maneuvering, maneuvering, clutter and in the presence of object existence uncertainty.
Introduction to smoothing
Filters, introduced in previous chapters, produce the “best estimate” of the object state at a particular time based on the measurements collected up to that time. Smoothers, on the other hand, produce an estimate of the state at a time based on measurements collected beyond the time in question (the predictor is another estimator where the estimation at a certain time is carried out based on measurements collected until a point before that time).
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- Information
- Fundamentals of Object Tracking , pp. 265 - 288Publisher: Cambridge University PressPrint publication year: 2011