A Comparison of Detection Performance for Several Track-before-Detect Algorithms

By S. Davey, Mark G. Rutten, Brian Cheung. Fusion, 2008

318 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/deb44c860b4ea1204620aca03637ee5775ca39dd

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In Artificial Intelligence

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Joint Detection and Estimation of Multiple Objects From Image Observations (2010, methodology): “This scenario also serves to demonstrate that our implementatio of H-PMHT is consistent with that in the survey [4].”