TY - JOUR
T1 - Adaptive subspace tests for multichannel signal detection in auto-regressive disturbance
AU - Gao, Yongchan
AU - Li, Hongbin
AU - Himed, Braham
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - This paper deals with the problem of detecting a subspace signal in the presence of spatially and temporally colored disturbance. A new subspace parametric signal model that takes into account a multi-rank subspace structure for the target signal and employs a multi-channel auto-regressive process for the disturbance signal is proposed. Following this model, a new subspace parametric Rao detector (SP-Rao) is developed for training-limited scenarios. Unlike conventional parametric detectors that are designed for only rank-one signal detection, the SP-Rao has a new multi-rank structure with a pairwise successive spatio-temporal whitening and cross-correlation process between the observed signal and each subspace basis vector. Additionally, a non-parametric subspace detector (NSD) is derived based upon a frequency-domain representation of the SP-Rao test statistic. The NSD is distinctively different from conventional subspace detectors, in which the former involves pairwise whitening and cross-correlation between the test signal and each subspace basis vector but the latter employs the whole subspace matrix. Numerical results are presented to illustrate the performance of the proposed subspace detectors in comparison with several leading existing methods, especially in the case of limited data.
AB - This paper deals with the problem of detecting a subspace signal in the presence of spatially and temporally colored disturbance. A new subspace parametric signal model that takes into account a multi-rank subspace structure for the target signal and employs a multi-channel auto-regressive process for the disturbance signal is proposed. Following this model, a new subspace parametric Rao detector (SP-Rao) is developed for training-limited scenarios. Unlike conventional parametric detectors that are designed for only rank-one signal detection, the SP-Rao has a new multi-rank structure with a pairwise successive spatio-temporal whitening and cross-correlation process between the observed signal and each subspace basis vector. Additionally, a non-parametric subspace detector (NSD) is derived based upon a frequency-domain representation of the SP-Rao test statistic. The NSD is distinctively different from conventional subspace detectors, in which the former involves pairwise whitening and cross-correlation between the test signal and each subspace basis vector but the latter employs the whole subspace matrix. Numerical results are presented to illustrate the performance of the proposed subspace detectors in comparison with several leading existing methods, especially in the case of limited data.
KW - Adaptive signal detection
KW - Rao test
KW - multi-channel auto-regressive model
KW - space-time adaptive processing
KW - subspace signal detection
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U2 - 10.1109/TSP.2018.2869123
DO - 10.1109/TSP.2018.2869123
M3 - Article
AN - SCOPUS:85053128953
SN - 1053-587X
VL - 66
SP - 5577
EP - 5587
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 21
M1 - 8457288
ER -