Kalman filters have been used as a robust method for object location prediction in various tracking algorithms for
nearly a decade. More recently, adaptive and extended Kalman filters have been employed, making predictions
even more reliable. The presented addition to this trend is the employment of a polynomial fit to the history of
object locations, using the adaptive Kalman filter framework. This allows the linear state model of the adaptive
Kalman filter to predict non-linear motion, making tracking more robust. This modified filter will be used in
conjunction with the Mean Shift algorithm as the measurement step. Another important consideration when
using a Kalman filter in this manner will be which correlation coefficient is used. The Pearson product-moment
correlation coefficient is shown to provide more robust tracking when compared to the Bhattacharyya coefficient
when objects have either low resolution or are unresolved.
The development and testing of thermal signature tracking algorithms burdens the developer with a method o f testing the
algorith m's fidelity. Collected video is normally used for testing tracking algorithms to evaluate performance in a variety
of configurations. Acquiring suitable volumes of collected video data in multiple configurations can be prohibitive. As
an alternative to collected video, the development of accurate synthetic thermal infrared vehicle models are incorporated
into background infrared scenes generated using the Digital Image and Remote Sensing Image Generat ion (DIRSIG)
software package. Additional software models for thermally emissive targets and motion are being implemented. The
goals are to accurately incorporate thermal signatures of moving targets into realistic radiomet rically calibrated scenes.
This aids in evaluating tracking algorithms using both visible and thermal infrared signatures for improved day and night
detection capability. The software packages are integrated together to produce synthetic video.
The development and testing of thermal signature tracking algorithms burdens the developer with a method
of testing the algorithm's fidelity. Although actual video is normally used for testing tracking algorithms, to
evaluate performance in a variety of configurations, the acquisition of suitable video data volume is
prohibitive. As an alternative to actual video we are developing accurate synthetic thermal infrared models
of vehicles that will be incorporated into background infrared images generated using the Digital Image and
Remote Sensing Image Generation (DIRSIG) software package. Motion for the targets within the
background scene is generated using the open-source Simulation of Urban MObility (SUMOTM) software
package. ThermoAnalytics' Multi-Service Electro-optic Signature (MuSESTM) software package is used to
model thermal emission from the object of interest. The goal is to accurately incorporate thermal signatures
of moving targets into realistic radiometrically calibrated scenes, and to then test and evaluate tracking
algorithms using both visible and thermal infrared signatures for improved day and night detection
capability. The software packages have been integrated together for a synthetic video
In this paper we present an approach to integrate sensors to meet the demanding requirements of Quick Reaction
Capability (QRC) airborne programs. Traditional airborne sensors are generally highly integrated and incorporate
custom sensor technologies and interfaces. Custom solutions and new technologies often require significant engineering
to achieve a high technology readiness level (TRL) and to meet the overall mission objective. Our approach differs from
traditional approaches in that we strive to achieve an integrated solution through regular review, assessment, and
identification of relevant industry "best athlete" technologies. Attention is focused on solution providers that adhere to
standard interfaces and formats, incorporate non-proprietary techniques, are deemed highly-reliable/repeatable, and
enable assembly production. Processes and engineering tools/methods have traditionally been applied to dozens of
longer-acquisition space-based ISR programs over 50 years. We have recently leveraged these techniques to solve
airborne Intelligence, Surveillance and Reconnaissance (ISR) mission challenges. This presentation describes and
illustrates key aspects and examples of these techniques, solving real-world airborne mission needs.
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