Adaptive Subspace Tests for Multichannel Signal Detection in Auto-Regressive Disturbance
| Autor: | Braham Himed, Yongchan Gao, Hongbin Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
020301 aerospace & aeronautics
Basis (linear algebra) Computer science Detector 020206 networking & telecommunications 02 engineering and technology Signal law.invention Matrix (mathematics) 0203 mechanical engineering Autoregressive model law Signal Processing 0202 electrical engineering electronic engineering information engineering Test statistic Detection theory Electrical and Electronic Engineering Radar Algorithm Subspace topology Parametric statistics |
| Zdroj: | IEEE Transactions on Signal Processing. 66:5577-5587 |
| ISSN: | 1941-0476 1053-587X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|