In centralized multisensor tracking systems, there are out-of-sequence measurements （OOSMs） frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such...In centralized multisensor tracking systems, there are out-of-sequence measurements （OOSMs） frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the ＂negative-time measurement update＂ problem, which is quite common in real mulUsensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model （DDM） of the process noise, it holds true only in discreUzed continuous-time model （DCM）. One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 60402033)
文摘In centralized multisensor tracking systems, there are out-of-sequence measurements （OOSMs） frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the ＂negative-time measurement update＂ problem, which is quite common in real mulUsensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model （DDM） of the process noise, it holds true only in discreUzed continuous-time model （DCM）. One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.
