Learning of scanning strategies for electronic support using predictive state representations

Abstract

In Electronic Support, a receiver must monitor a wide frequency spectrum in which threatening emitters operate. A common approach is to use sensors with high sensitivity but a narrow bandwidth. To maintain surveillance over the whole spectrum, the sensor has to sweep between frequency bands but requires a scanning strategy. Search strategies are usually designed prior to the mission using an approximate knowledge of illumination patterns. This often results in open-loop policies that cannot take advantage of previous observations. As pointed out in past researches, these strategies lack of robustness to the prior. We propose a new closed loop search strategy that learns a stochastic model of each radar using predictive state representations. The learning algorithm benefits from the recent advances in spectral learning and rank minimization using nuclear norm penalization.

Sensor scheduling for electronic support to intercept beam‐agile radar

“…They note one exception. Glaude et al [4], acknowledging that ESA radars ‘can produce more or less random patterns’, remark that, nevertheless, ‘due to their scanning function [they] produce roughly periodic illuminations’.…”