Sequence comparison techniques for multisensor data fusion and target recognition

Model Based Fusion for Multisensor Target Recognition

A multisensor feature-based fusion approach to target recognition using a framework of model-theory is proposed. The Best Discrimination Basis Algorithm (BDBA) based on the best basis selection technique and the Sensory Data Fusion System (SDFS) based on logical models and theories are applied for feature extraction. The BDBA selects the most discriminant basis. The SDFS rst selects features, w...

Multisensor data fusion and decision support for airborne target identification

Abstruct-A knowledge-based approach and a reasoning system for multisensor data fusion (MSF) is presented. The scenario taken for the example is an air-land battlefield situation. A data fusion system obtains data from a variety of sensors. This is an essential step in a Command, Control, Communication and Intelligence (C31) system. Automatic processing of sensor data has become essential due t...

Multisensor Data Fusion

Reactive Tuning of Target Estimate Accuracy in Multisensor Data Fusion

Dealing with conflicting and target-specific requirements is an important issue in multi-sensor and multi-target tracking. This paper aims to allocate sensing resources among various targets in reaction to individual information requests. The approach proposed is to introduce agents for every relevant target responsible for its tracking. Such agents are expected to bargain with each other for a...

Multisensor Data Fusion

IMultisensor data fusion refers to the acquisition, processing and synergistic combination of information gathered by various knowledge sources and sensors to provide a better understanding of a phenomenon. It is a fascinating and rapidly evolving field that has generated a lot of excitement in the research and development community. These concepts are being applied to a wide variety of fields ...