Abstract
Out-of-sequence measurements (OOSMs) frequently arise in a multi-platform central tracking system due to delays in communication networks and varying pre-processing times at the sensor platforms. During the last few years, multiple-lag OOSM filtering algorithms have received a great deal of attention. However, a comparative analysis of these algorithms for multiple OOSMs is lacking. This paper analyzes a number of multiple-lag OOSM filtering algorithms in terms of optimality, accuracy, statistical consistency, and computational speed. These factors are important for realistic multi-target multi-sensor tracking systems. We examine the performance of these algorithms using a number of examples with Monte Carlo simulations. We present numerical results using simulated data, which includes two-dimensional position and velocity measurements.
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