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Comparing stochastic and Markov decision process approaches for predicting radio frequency interference
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Paper Abstract

This work evaluates the performance of a cognitive radar system which predicts and avoids radio frequency interference (RFI) through an alternating renewal process (ARP) model-based and Markov Decision Process (MDP) approach. As radio frequency (RF) environments grow more crowded, the need for such a system becomes necessary. The cognitive radar monitors the RF activity to train a model for RFI prediction and avoidance. By modeling activity as an alternating renewal process, the stochastic approach calculates the likelihood of interference from measured RFI statistics. Alternatively, the MDP uses reinforcement learning to determine the optimal sequence of decisions given measured RF activity. Both methods eventually select the widest radar transmit bandwidth to minimize interference. The performance of each approach is evaluated by the number of collisions and missed opportunities. A hardware implemented test-bed deploys both methods on a set of synthetic and real measured RFI spectra in real-time to compare performance with the goal of determining when each process is more beneficial (in terms of performance and complexity).

Paper Details

Date Published: 3 May 2019
PDF: 13 pages
Proc. SPIE 11003, Radar Sensor Technology XXIII, 1100318 (3 May 2019); doi: 10.1117/12.2519675
Show Author Affiliations
Jacob A. Kovarskiy, The Pennsylvania State Univ. (United States)
Mark Kozy, Virginia Polytechnic Institute and State Univ. (United States)
Charles Thornton, Virginia Polytechnic Institute and State Univ. (United States)
Anthony F. Martone, U.S. Army Research Lab. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)
R. Michael Buehrer, Virginia Polytechnic Institute and State Univ. (United States)
Kelly D. Sherbondy, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 11003:
Radar Sensor Technology XXIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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