TY - GEN
T1 - Interference Mitigation in Blind Source Separation by Hidden State Filtering
AU - Ghosh, Anushreya
AU - Haimovich, Alexander M.
AU - Dabin, Jason A.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Radio frequency (RF) sources are observed by a uniform linear array (ULA) in the presence of interference. The activity of the sources of interest is sparse, intermittent and assumed to follow a hidden Markov model (HMM). The interfering jammer is active during the entire period of observation. Blind Source Separation (BSS) is performed using direction of arrival (DOA) as criterion of separating the sources as well as the jammer. It is shown that an interfering jammer has a deleterious effect on the performance of the BSS. Leveraging the HMM activity model of the sources, a method is proposed to mitigate the effect of an interfering jammer. The proposed method is essentially a state filtering technique, and it is referred to as Hidden State Filtering (HSF). Two different HSF methods are introduced and compared. The HSF concept is extended to include estimating the HMM model parameters from the observed data. Numerical results demonstrate that the proposed approach is capable of mitigating the effects of interference and enhance source separation.
AB - Radio frequency (RF) sources are observed by a uniform linear array (ULA) in the presence of interference. The activity of the sources of interest is sparse, intermittent and assumed to follow a hidden Markov model (HMM). The interfering jammer is active during the entire period of observation. Blind Source Separation (BSS) is performed using direction of arrival (DOA) as criterion of separating the sources as well as the jammer. It is shown that an interfering jammer has a deleterious effect on the performance of the BSS. Leveraging the HMM activity model of the sources, a method is proposed to mitigate the effect of an interfering jammer. The proposed method is essentially a state filtering technique, and it is referred to as Hidden State Filtering (HSF). Two different HSF methods are introduced and compared. The HSF concept is extended to include estimating the HMM model parameters from the observed data. Numerical results demonstrate that the proposed approach is capable of mitigating the effects of interference and enhance source separation.
KW - Blind Source Separation
KW - Direction of Arrival
KW - Hidden Markov Model
KW - Jammer
KW - Sparse Representation
UR - http://www.scopus.com/inward/record.url?scp=85154056607&partnerID=8YFLogxK
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U2 - 10.1109/CISS56502.2023.10089636
DO - 10.1109/CISS56502.2023.10089636
M3 - Conference contribution
AN - SCOPUS:85154056607
T3 - 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023
BT - 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th Annual Conference on Information Sciences and Systems, CISS 2023
Y2 - 22 March 2023 through 24 March 2023
ER -