A comparison of the tracking performance of some distributed multisensor data fusion algorithms based on Kalman filter methods

This paper deals with data fusion algorithms used for multi-sensor tracking used in the context of deployable autonomous distributed system (DADS). DADS is an advanced tactical/surveillance system that operates as an autonomous field of underwater-distributed sensor nodes. It is comprised of a network of intelligent battery-powered sensors that can detect and track a moving object in space. Control of operating modes and sensor-to-sensor communications is needed to optimize the performance and operating life of the sensor field. The principal objective of this project is to develop exemplary fusion algorithms for tracking in a multisensor system to facilitate the development and evaluation of approaches for distributed control of the sensor system. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman filter methods. We assume that the sensor has already detected a target with range and bearing measurements.