Analytic combinatorics in multiple object tracking
- Resource Type
- Conference
- Authors
- Streit, Roy
- Source
- 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2017. :1-6 Oct, 2017
- Subject
- Aerospace
Signal Processing and Analysis
Clutter
Probability density function
Probability distribution
Object tracking
Handheld computers
Bayes methods
Complexity theory
Analytic combinatorics
Palm process
Reduced Palm process
Interval smoothing
MultiBernoulli
Saddle point
- Language
The method of analytic combinatorics (AC) is a unified approach to multiple object tracking that encodes joint probability distributions into probability generating functionals (PGFLs). PGFLs characterize distributions exactly. A high level view of the tracking applications of PGFLs is outlined in this paper. Assignment models in well-known filters are modeled as products of PGFLs. MHT and multiBernoulli PGFLs are compared. Track extraction and the “notched” filter of the (reduced) Palm process are discussed. Bounded complexity approximate particle filter weights are found by saddle point methods applied to the Cauchy integral form of the derivatives.