Centralized multiple-view sensor fusion using labeled multi-Bernoulli filters

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Abstract This paper presents a novel method for track-to-track fusion to integrate multiple-view sensor data in a centralized sensor network. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which considers constant weights allocated for sensors. We introduce an intuitive approach to automatically tune the weights in the Generalized Covariance Intersection method based on the amount of information carried by the posteriors that are locally computed from measurements acquired at each sensor node. To quantify information content, Cauchy–Schwarz divergence is used. Our solution is particularly formulated for sensor networks where the update step of a Labeled Multi-Bernoulli filter is running locally at each node. We will show that with that type of filter, the weight associated with each sensor node can be separately adapted for each Bernoulli component of the filter. The results of numerical experiments show that our proposed method can successfully integrate information provided by multiple sensors with different fields of view. In such scenarios, our method significantly outperforms the common approach of using Generalized Covariance Intersection method with constant weights, in terms of inclusion of all existing objects and tracking accuracy.