A comparison of detection performance for several Track-Before-Detect algorithms

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 (TkBD) 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 TkBD problem. This paper 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 Multi-Hypothesis Tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.