| Title |
A Tight Bound for PDA-AI Performance |
| Authors |
김국민(Kim Kook-Min) ; 송택렬(Song Taek-Lyul) ; 안조영(Ahn Jo-Young) |
| Keywords |
Target tracking ; PDA-AI ; Monte Carlo simulation ; Tight lower bound ; Analytic solution |
| Abstract |
In this paper, We propose a new target tracking filter which utilizes PDA-AI for data association in a clutter environment and also propose an analytic solution for ideal filter covariance which accounts for all the possible events in PDA-AI. Monte Carlo simulation for the proposed filter in a clutter environment indicates that the proposed analytic solution forms a tight lower bound for the error covariance of PDA-AI filter. |