A comparison of detection performance for several track-before-detect algorithms

Authors:
Samuel J. Davey;Mark G. Rutten;Brian Cheung
Affiliations:
Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia;Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia;Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

Citing 3
Cited 2

Bayesian Multiple Target Tracking

Bayesian Multiple Target Tracking
Bayesian methods for multiaspect target tracking in image sequences

IEEE Transactions on Signal Processing
Offline and Real-Time Methods for ML-PDA Track Validation

IEEE Transactions on Signal Processing

Relative entropy rate based multiple hidden Markov model approximation

IEEE Transactions on Signal Processing
Joint detection and estimation of multiple objects from image observations

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.