Data Fusion for Improved Acquisition, Tracking and Discrimination

Target discrimination is the ability to select a desired target in the presence of multiple targets. Unfortunately, current passive infrared (IR) missile sensors are not adequate to discriminate between decoys, penetration aids, and targets based upon anindividual sensor signature. Acquiring a target in a noisy or cluttered environment is a difficult task. Sensor noise and background clutter can mask the target. If information is available from only one sensor, targets can be obscured and hidden fromthe sensor. SEAKR Engineering will develop and conduct proof-of-principle demonstrations of advanced data fusion concepts that aid in the target acquisition, tracking, and discrimination problem. While taking into account the variety and disparity ofsensor platforms, algorithms will be chosen and developed that effectively fuse the information from ground, satellite, and onboard sensors. The following algorithms, software, and hardware will be evaluated during Phase I of this SBIR: Optimal NonlinearFiltering, Automatic Multisensor Feature-based Recognition System, Distributed Multisensor Fusion, Loglikelihood Data Fusion, Sequential and Parallel Implementations, Software Sensor Control and Management, Distributed and Centralized Processing, andSEAKR's Re-configurable Computer. Multisensor, data fusion algorithms are of use in many fields including medical imaging, air traffic control, tactical air defense, robotics, computer vision, and other systems where measurements from multiple sensors areused to estimate the states of multiple objects. During Phase I of this research, SEAKR Engineering will define and develop potential data fusion concepts that aid in the target discrimination problem