Intelligent processing techniques for sensor fusion

Katherine A. Byrd; Bart Smith; Doug Allen; Norman Morris; Charles A. Bjork Jr.; Kim Deal-Giblin; John A. Rushing

doi:10.1117/12.303669

20 March 1998 Intelligent processing techniques for sensor fusion

Katherine A. Byrd, Bart Smith, Doug Allen, Norman Morris, Charles A. Bjork Jr., Kim Deal-Giblin, John A. Rushing

Proceedings Volume 3376, Sensor Fusion: Architectures, Algorithms, and Applications II; (1998) https://doi.org/10.1117/12.303669
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Intelligent processing techniques which can effectively combine sensor data from disparate sensors by selecting and using only the most beneficial individual sensor data is a critical element of exoatmospheric interceptor systems. A major goal of these algorithms is to provide robust discrimination against stressing threats in poor a priori conditions, and to incorporate adaptive approaches in off- nominal conditions. This paper summarizes the intelligent processing algorithms being developed, implemented and tested to intelligently fuse data from passive infrared and active LADAR sensors at the measurement, feature and decision level. These intelligent algorithms employ dynamic selection of individual sensors features and the weighting of multiple classifier decisions to optimize performance in good a priori conditions and robustness in poor a priori conditions. Features can be dynamically selected based on an estimate of the feature confidence which is determined from feature quality and weighting terms derived from the quality of sensor data and expected phenomenology. Multiple classifiers are employed which use both fuzzy logic and knowledge based approaches to fuse the sensor data and to provide a target lethality estimate. Target designation decisions can be made by fusing weighted individual classifier decisions whose output contains an estimate of the confidence of the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data or to request additional sensor data on specific objects that have not been confidently identified as being lethal or non- lethal. The algorithms are implemented in C within a graphic user interface framework. Dynamic memory allocation and the sequentialy implementation of the feature algorithms are employed. The baseline set of fused sensor discrimination algorithms with intelligent processing are described in this paper. Example results from the algorithms are shown based on static range sensor measurement data.

Citation Download Citation

Katherine A. Byrd, Bart Smith, Doug Allen, Norman Morris, Charles A. Bjork Jr., Kim Deal-Giblin, and John A. Rushing "Intelligent processing techniques for sensor fusion", Proc. SPIE 3376, Sensor Fusion: Architectures, Algorithms, and Applications II, (20 March 1998); https://doi.org/10.1117/12.303669

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