Automatic modulation classification (AMC) is a critical prerequisite for demodulation of communication signals in tactical scenarios. Depending on the number of unknown parameters involved, the complexity of AMC can be prohibitive. Existing maximum-likelihood and feature-based approaches rely on centralized processing. The present paper develops AMC algorithms using spatially distributed sensors, each acquiring relevant features of the received signal. Individual sensors may be unable to extract all relevant features to reach a reliable classification decision. However, the cooperative in-network approach developed enables high classification rates at reduced-overhead, even when features are noisy and/or missing. Simulated tests illustrate the performance of the novel distributed AMC scheme.
|Original language||English (US)|
|Title of host publication||2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success|
|State||Published - Dec 1 2008|
|Event||2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success - Washington, DC, United States|
Duration: Nov 17 2008 → Nov 19 2008
|Other||2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success|
|Period||11/17/08 → 11/19/08|