Optimal Update with Multistep Out-of-Sequence Measurements in Target Tracking

In centralized multisensor tracking systems, there are out-of-sequence measurement (OOSM) problems frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrivals can induce "negative-time measurement update" problem, which is quite common in real multisensor tracking systems. Recently, the optimal update with multistep OOSMs denoted as algorithm Zl was presented. However, this paper shows the optimality of algorithm Zl is true only in simple OOSM scenario and discretized continuous-time model (DCM) of the process noise. Otherwise it is not optimal. A new optimal OOSM filtering algorithm with multistep OOSMs based on information filter is presented, which is independent of the discrete time model of the process noise and is optimal for different OOSM scenarios. It is shown that the performance of the new optimal algorithm is better than that of algorithm Zl by numerical results.