Tensor-Based Efficient Multi-Interferer RFI Excision Algorithms for SIMO Systems

Radio frequency interference (RFI) is causing performance loss in microwave radiometry, radio astronomy, and satellite communications. As the number of interferers increases, the performance loss gets more severe and RFI excision becomes more difficult. In this regard, this paper introduces the multilinear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for single-input multiple-output (SIMO) systems suffering from MI-RFI. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm, which enhances MLSEP, is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibit a faster convergence to a zero excision error than MLSEP which, in turn converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller.

[1]  M. Alex O. Vasilescu,et al.  Multilinear Projection for Appearance-Based Recognition in the Tensor Framework , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Abdelhak M. Zoubir,et al.  Source Enumeration in Array Processing Using a Two-Step Test , 2015, IEEE Transactions on Signal Processing.

[3]  Claudio Maccone,et al.  The KLT (Karhunen–Loève Transform) to extend SETI searches to broad-band and extremely feeble signals , 2010 .

[4]  Nicholas Kalouptsidis,et al.  Subspace projection based blind channel order estimation of MIMO systems , 2006, IEEE Transactions on Signal Processing.

[5]  Florian Roemer,et al.  Higher-Order SVD-Based Subspace Estimation to Improve the Parameter Estimation Accuracy in Multidimensional Harmonic Retrieval Problems , 2008, IEEE Transactions on Signal Processing.

[6]  F. Li,et al.  Performance analysis for DOA estimation algorithms: unification, simplification, and observations , 1993 .

[7]  Daniele Borio,et al.  Time-Frequency Excision for GNSS Applications , 2008, IEEE Systems Journal.

[8]  Finding the Interference Karhunen-Loève Transform as an Instrument to Detect Weak RF signals , .

[9]  Xiaoli Ma,et al.  First-Order Perturbation Analysis of Singular Vectors in Singular Value Decomposition , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[10]  Karl F. Warnick,et al.  Auxiliary antenna-assisted interference mitigation for radio astronomy arrays , 2005, IEEE Transactions on Signal Processing.

[11]  Ting Li,et al.  HF Radio-Frequency Interference Mitigation , 2010, IEEE Geoscience and Remote Sensing Letters.

[12]  Karl F. Warnick,et al.  Model-Based Subspace Projection Beamforming for Deep Interference Nulling , 2012, IEEE Transactions on Signal Processing.

[13]  Florian Roemer,et al.  Blind estimation of SIMO channels using a tensor-based subspace method , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[14]  Wessam Ajib,et al.  Efficient Semi-Blind Channel Estimators for SIMO Systems Suffering from Broadband RFI , 2015, 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB).

[15]  Sidharth Misra,et al.  Microwave Radiometer Radio-Frequency Interference Detection Algorithms: A Comparative Study , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Fabio Dovis,et al.  Recent Trends in Interference Mitigation and Spoofing Detection , 2012, Int. J. Embed. Real Time Commun. Syst..

[17]  K. Abend,et al.  Interference suppression via orthogonal projections: a performance analysis , 1993 .

[18]  Albert-Jan Boonstra,et al.  Oblique projection beamforming for RFI mitigation in radio astronomy , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[19]  Pau Closas,et al.  Antenna Array Based GNSS Signal Acquisition for Interference Mitigation , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Sidharth Misra,et al.  RFI detection and mitigation for microwave radiometry with an agile digital detector , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Pau Closas,et al.  Multi-antenna techniques for interference mitigation in GNSS signal acquisition , 2013, EURASIP J. Adv. Signal Process..

[22]  Cram,et al.  Discrete-time signal processing : Alan V. Oppenheim, 3rd edition , 2011 .

[23]  Joel T. Johnson,et al.  Examination of a simple pulse blanking technique for RFI mitigation , 2004 .

[24]  Florian Roemer,et al.  Analytical Performance Assessment of Multi-Dimensional Matrix- and Tensor-Based ESPRIT-Type Algorithms , 2014, IEEE Transactions on Signal Processing.

[25]  Wessam Ajib,et al.  Multi-linear subspace estimation and projection for efficient RFI excision in SIMO systems , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[26]  Florian Roemer,et al.  Analytical performance evaluation for HOSVD-based parameter estimation schemes , 2009, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[27]  Joel T. Johnson,et al.  Time and Frequency Blanking for Radio-Frequency Interference Mitigation in Microwave Radiometry , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Alle-Jan van der Veen,et al.  Performance analysis of spatial filtering of RF interference in radio astronomy , 2005, IEEE Trans. Signal Process..

[29]  Joel T. Johnson,et al.  Examination of a simple pulse‐blanking technique for radio frequency interference mitigation , 2005 .

[30]  Ignacio Santamaría,et al.  Effective channel order estimation based on combined identification/equalization , 2006, IEEE Transactions on Signal Processing.

[31]  G. Strang Introduction to Linear Algebra , 1993 .

[32]  F. Roemer,et al.  Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing , 2013 .

[33]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..