Multiple Target Tracking using Recursive RANSAC

Estimating the states of multiple dynamic targets is difficult due to noisy and spurious measurements, missed detections, and the interaction between multiple maneuvering targets. In this paper a novel algorithm, which we call the recursive random sample consensus (R-RANSAC) algorithm, is presented to robustly estimate the states of an unknown number of dynamic targets. R-RANSAC was previously developed to estimate the parameters of multiple static signals when measurements are received sequentially in time. The R-RANSAC algorithm proposed in this paper reformulates our previous work to track dynamic targets using a Kalman filter. Simulation results using synthetic data are included to compare R-RANSAC to the GM-PHD filter.