TY - GEN
T1 - Adaptive signal detection in subspace interference with partial prior knowledge
AU - Jiang, Yuan
AU - Li, Hongbin
AU - Fang, Jun
AU - Rangaswamy, Muralidhar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - We consider the problem of weak signal detection in strong disturbance with a subspace structure. Unlike conventional subspace detection techniques relying on the availability of a large amount of training data, we consider a knowledge-aided (KA) subspace detection approach for training limited scenarios by incorporating partial prior knowledge of the subspace. A unique feature of the proposed approach is that it can identify the missing subspace bases and recover the full subspace structure by using only the test signal, thus bypassing the need for training data. The proposed approach utilizes a Bayesian hierarchical model for knowledge representation. The model is integrated within a sparse Bayesian framework, which promotes parsimonious subspace representation of the observed data. A variational Bayesian inference algorithm is developed based on the proposed model to recover parameters and subspace structures associated with the disturbance, which are then brought into a generalized likelihood ratio test (GLRT) to perform signal detection. Numerical results are presented to illustrate the performance of the proposed subspace detector in comparison with several notable existing methods.
AB - We consider the problem of weak signal detection in strong disturbance with a subspace structure. Unlike conventional subspace detection techniques relying on the availability of a large amount of training data, we consider a knowledge-aided (KA) subspace detection approach for training limited scenarios by incorporating partial prior knowledge of the subspace. A unique feature of the proposed approach is that it can identify the missing subspace bases and recover the full subspace structure by using only the test signal, thus bypassing the need for training data. The proposed approach utilizes a Bayesian hierarchical model for knowledge representation. The model is integrated within a sparse Bayesian framework, which promotes parsimonious subspace representation of the observed data. A variational Bayesian inference algorithm is developed based on the proposed model to recover parameters and subspace structures associated with the disturbance, which are then brought into a generalized likelihood ratio test (GLRT) to perform signal detection. Numerical results are presented to illustrate the performance of the proposed subspace detector in comparison with several notable existing methods.
KW - Bayesian interference
KW - Knowledge-aided processing
KW - Radar applications
KW - Subspace signal detection
UR - http://www.scopus.com/inward/record.url?scp=85021404647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021404647&partnerID=8YFLogxK
U2 - 10.1109/RADAR.2017.7944330
DO - 10.1109/RADAR.2017.7944330
M3 - Conference contribution
AN - SCOPUS:85021404647
T3 - 2017 IEEE Radar Conference, RadarConf 2017
SP - 897
EP - 902
BT - 2017 IEEE Radar Conference, RadarConf 2017
T2 - 2017 IEEE Radar Conference, RadarConf 2017
Y2 - 8 May 2017 through 12 May 2017
ER -