This paper provides an introduction to sensor fusion techniques for target tracking. It presents an overview of common filtering techniques that are effective for moving targets as well as methods of overcoming problems specific to target tracking, such as measurement-to-track association and sensor registration. The computational demand of such algorithms is discussed and various practices, including distributed processing of target tracks and sensor management, are proposed to help reduce this demand. Final comments include a discussion of applications and implementation issues specific to the presented scenarios.

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This paper addresses the problem of ranking and selection for stochastic processes, such as target tracking algorithms, where variance is the performance metric. Comparison of different tracking algorithms or parameter sets within one algorithm relies on time-consuming and computationally demanding simulations. We present a method to minimize simulation time, yet to achieve a desirable confidence of the obtained results by applying ordinal optimization and computing budget allocation ideas and techniques, while taking into account statistical properties of the variance. The developed method is applied to a general tracking problem of sensors tracking targets using a sequential multi-sensor data fusion tracking algorithm. The optimization consists of finding the order of processing sensor information that results in the smallest variance of the position error. Results that we obtained with high confidence levels and in reduced simulation times confirm the findings from our previous research (where we considered only two sensors) that processing the best available sensor the last performs the best, on average. The presented method can be applied to any ranking and selection problem where variance is the performance metric.