Classification of rf transients in space using digital signal processing and neural network techniques

Kurt R. Moore; Phil C. Blain; Scott D. Briles; Rollin G. Jones

doi:10.1117/12.205096

6 April 1995 Classification of rf transients in space using digital signal processing and neural network techniques

Kurt R. Moore, Phil C. Blain, Scott D. Briles, Rollin G. Jones

Author Affiliations +

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205096
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

The FORTE' (fast on-orbit recording of transient events) small satellite experiment scheduled for launch in October 1995 will attempt to measure and classify electromagnetic transients as sensed from space. The FORTE' payload will employ an event classifier to perform onboard classification of radio frequency transients from terrestrial sources such as lightning. These transients are often dominated by a constantly changing assortment of man-made `clutter' such as TV, FM, and radar signals. The FORTE' event classifier, or EC, uses specialized hardware to implement various signal processing and neural network algorithms. The resulting system can process and classify digitized records of several thousand samples onboard the spacecraft at rates of about a second per record. In addition to reducing downlink rates, the EC minimizes command uplink data by normally using uploaded algorithm sequences rather than full code modules (although it is possible for full code modules to be uploaded from the ground). The FORTE' event classifier experiment combines science and engineering in an evolutionary step toward useful and robust adaptive processing systems in space.

Citation Download Citation

Kurt R. Moore, Phil C. Blain, Scott D. Briles, and Rollin G. Jones "Classification of rf transients in space using digital signal processing and neural network techniques", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205096

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