Joint target detection and tracking algorithm for shave-scan optical sensor

A random finite sets (RFS) theory based joint detection and tracking algorithm was proposed for detecting dim small moving target and estimating its state parameters from scan image sequences. By analyzing the scan characteristics of shave-scan optical sensor, a target dynamic model and observation model were established, respectively. Then target state and measurements was described as a RFS variable. The joint detection and tracking problem was modeled as a Bayesian optimal estimation problem. Prediction and updating formulas of this algorithm were derived using RFS theory. The algorithm implementation problem was taken into account. A Gaussian mixture (GM) implementation is presented. Simulation results show that this algorithm can depress clutters strongly while has small influence on missing detections. It can accomplish the target detection and tracking task efficiently for shave-scan optical sensor.