Multi-level sensor fusion for improved target discrimination

A project was sponsored by the US Army Space and Strategic Defense Command (USASSDC) to develop, test, and demonstrate sensor fusion techniques for target recognition. The sensor fusion architecture developed for this program exploits the use of sensor fusion at all levels (signal, feature, and decision levels) to improve target recognition capability against tactical ballistic missile (TBM) targets. It was hypothesized that use of fusion at all these levels is necessary to give a system robustness to noise and sensor degradation and to accommodate a wide variety of mission and target types. This would address problems such as ineffective sensors, missing data, and invalid classifier assumptions. Thus, the critical objective of the new fusion system architecture is to permit graceful degradation rather than catastrophic failure whenever some components of the system, whether hardware or software,fail to perform adequately. Two tests were conducted on the trained discrimination algorithms: a blind test with simulated noisy signatures collected at angles different from those in the training database and a test using actual radar field test data collected during flight of a representative TBM target. The test results demonstrate a high degree of recognition accuracy. The paper describes the training and testing techniques used, show the fusion strategy employed, and illustrate the advantages of exploiting multi-level fusion.