TY - GEN
T1 - Weak signal sensing using empirical mode decomposition and stochastic data reordering
AU - Roy, Arnab
AU - Doherty, John F.
PY - 2011
Y1 - 2011
N2 - A weak signal sensing technique that exploits the dyadic filter-bank property of the empirical mode decomposition (EMD) technique for noise-dominated signals is presented in this paper. The EMD procedure decomposes wideband noise into a series of constituents with linearly decreasing mean energy and mean frequency on the logarithmic scale, and the energy of the appropriate modes, corresponding to frequency subbands, constitutes our signal feature. A weak stochastic signal contributes to the energy of certain modes that correspond to the frequency content of the signal, which can be used to detect their presence. The effectiveness of this technique is further enhanced via local stochastic reordering of the original data samples that generates new noise realizations while affecting the stochastic part negligibly. Averaging the signal features obtained using the nonlinear decomposition over multiple reordering realizations improves signal detection reliability. This novel application of the EMD procedure is described in this paper and its performance compared against standard signal detection techniques.
AB - A weak signal sensing technique that exploits the dyadic filter-bank property of the empirical mode decomposition (EMD) technique for noise-dominated signals is presented in this paper. The EMD procedure decomposes wideband noise into a series of constituents with linearly decreasing mean energy and mean frequency on the logarithmic scale, and the energy of the appropriate modes, corresponding to frequency subbands, constitutes our signal feature. A weak stochastic signal contributes to the energy of certain modes that correspond to the frequency content of the signal, which can be used to detect their presence. The effectiveness of this technique is further enhanced via local stochastic reordering of the original data samples that generates new noise realizations while affecting the stochastic part negligibly. Averaging the signal features obtained using the nonlinear decomposition over multiple reordering realizations improves signal detection reliability. This novel application of the EMD procedure is described in this paper and its performance compared against standard signal detection techniques.
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U2 - 10.1109/MILCOM.2011.6127697
DO - 10.1109/MILCOM.2011.6127697
M3 - Conference contribution
AN - SCOPUS:84856973546
SN - 9781467300810
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 37
EP - 41
BT - 2010 Military Communications Conference, MILCOM 2010
T2 - 2011 IEEE Military Communications Conference, MILCOM 2011
Y2 - 7 November 2011 through 10 November 2011
ER -