Visible to the public Situational Awareness

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Situational awareness is an important human factor for cyber security. IEEE published a Special Issue on Signal Processing for Situational Awareness from Networked Sensors and Social Media in April 2014: Signal Processing, IEEE Transactions on, Volume 62 , Issue: 4, April 1, 2014 , Page(s): 1035. Many of the citations are from this special issue.

Note: IEEE has published a Special Issue on Signal Processing for Situational Awareness from Networked Sensors and Social Media this month: Signal Processing, IEEE Transactions on, Vol 62 , Issue: 4, April 1, 2014 , Page(s): 1035. Many of the citations below are from this special issue.

  • "Robust Beamforming by Linear Programming," Jiang, X.; Zeng, W.-J.; Yasotharan, A.; So, H.C.; Kirubarajan, T., Signal Processing, IEEE Transactions on , vol.62, no.7, pp.1834,1849, April1, 2014. (ID#:14-1294) Available at: In this paper, a robust linear programming beamformer (RLPB) is proposed for non-Gaussian signals in the presence of steering vector uncertainties. Unlike most of the existing beamforming techniques based on the minimum variance criterion, the proposed RLPB minimizes the $ell_{infty}$-norm of the output to exploit the non-Gaussianity. We make use of a new definition of the $ell_{p}$-norm $(1leq pleqinfty)$ of a complex-valued vector, which is based on the $ell_{p}$-modulus of complex numbers. To achieve robustness against steering vector mismatch, the proposed method constrains the $ell_{infty}$ -modulus of the response of any steering vector within a specified uncertainty set to exceed unity. The uncertainty set is modeled as a rhombus, which differs from the spherical or ellipsoidal uncertainty region widely adopted in the literature. The resulting optimization problem is cast as a linear programming and hence can be solved efficiently. The proposed RLPB is computationally simpler than its robust counterparts requiring solution to a second-order cone programming. We also address the issue of appropriately choosing the uncertainty region size. Simulation results demonstrate the superiority of the proposed RLPB over several state-of-the-art robust beamformers and show that its performance can approach the optimal performance bounds.
  • "Secrecy Wireless Information and Power Transfer With MISO Beamforming," Liu, L.; Zhang, R.; Chua, K.-C., Signal Processing, IEEE Transactions on , vol.62, no.7, pp.1850,1863, April 1, 2014. (ID#:14-1295) Available at: The dual use of radio signal for simultaneous wireless information and power transfer (SWIPT) has recently drawn significant attention. To meet the practical requirement that the energy receiver (ER) operates with significantly higher received power as compared to the conventional information receiver (IR), ERs need to be deployed in more proximity to the transmitter than IRs in the SWIPT system. However, due to the broadcast nature of wireless channels, one critical issue arises that the messages sent to IRs can be eavesdropped by ERs, which possess better channels from the transmitter. In this paper, we address this new physical-layer security problem in a multiuser multiple-input single-output (MISO) SWIPT system where one multi-antenna transmitter sends information and energy simultaneously to an IR and multiple ERs, each with one single antenna. Two problems are investigated with different practical aims: the first problem maximizes the secrecy rate for the IR subject to individual harvested energy constraints of ERs, while the second problem maximizes the weighted sum-energy transferred to ERs subject to a secrecy rate constraint for IR. We solve these two non-convex problems optimally by a general two-stage procedure. First, by fixing the signal-to-interference-plus-noise ratio (SINR) target for ERs or IR, we obtain the optimal transmit beamforming and power allocation solution by applying the technique of semidefinite relaxation (SDR). Then, each of the two problems is solved by a one-dimension search over the optimal SINR target for ERs or IR. Furthermore, for each problem, suboptimal solutions of lower complexity are proposed.
  • "Minimum Dispersion Beamforming for Non-Gaussian Signals," Jiang, X.; Zeng, W.-J.; Yasotharan, A.; So, H.C.; Kirubarajan, T., Signal Processing, IEEE Transactions on , vol.62, no.7, pp.1879,1893, April1, 2014. (ID#:14-1296) Available at: Most of the existing beamforming methods are based on the Minimum Variance (MV) criterion. The MV approach is statistically optimal only when the signal, interferences and the noise are Gaussian-distributed. However, non-Gaussian signals arise in a variety of practical applications. In this paper, Minimum Dispersion Distortionless Response (MDDR) beamforming, which minimizes the $ell_p$-norm of the output while constraining the desired signal response to be unity, is devised for non-Gaussian signals. It is shown that the MDDR beamformer, which implicitly exploits non-Gaussianity, can improve the performance significantly if $p > 2$ for sub-Gaussian signals or $p < 2$ for super-Gaussian signals. Three efficient algorithms, the Iteratively Reweighted Minimum Variance Distortionless Response (IR-MVDR), complex-valued full Newton's and partial Newton's methods, are developed to solve the resulting $ell_p$ -norm minimization with a linear constraint. Furthermore, the MDDR beamformer with a single constraint is generalized to the Linearly Constrained Minimum Dispersion (LCMD) beamformer with multiple linear constraints, which exhibits robustness against steering vector mismatch. The LCMD beamformer yields significant performance improvement over the conventional Linearly Constrained Minimum Variance (LCMV) beamformer. Simulation results are provided to demonstrate the superior performance of the proposed minimum dispersion beamforming approaches.
  • "Greedy Algorithms for Joint Sparse Recovery," Blanchard, J.D.; Cermak, M.; Hanle, D.; Jing, Y.,Signal Processing, IEEE Transactions on , vol.62, no.7, pp.1694,1704, April 1, 2014. (ID#:14-1297) Available at: Five known greedy algorithms designed for the single measurement vector setting in compressed sensing and sparse approximation are extended to the multiple measurement vector scenario: Iterative Hard Thresholding (IHT), Normalized IHT (NIHT), Hard Thresholding Pursuit (HTP), Normalized HTP (NHTP), and Compressive Sampling Matching Pursuit (CoSaMP). Using the asymmetric restricted isometry property (ARIP), sufficient conditions for all five algorithms establish bounds on the discrepancy between the algorithms' output and the optimal row-sparse representation. When the initial multiple measurement vectors are jointly sparse, ARIP-based guarantees for exact recovery are also established. The algorithms are then compared via the recovery phase transition framework. The strong phase transitions describing the family of Gaussian matrices which satisfy the sufficient conditions are obtained via known bounds on the ARIP constants. The algorithms' empirical weak phase transitions are compared for various numbers of multiple measurement vectors. Finally, the performance of the algorithms is compared against a known rank aware greedy algorithm, Rank Aware Simultaneous Orthogonal Matching Pursuit + MUSIC. Simultaneous recovery variants of NIHT, NHTP, and CoSaMP all outperform the rank-aware algorithm.
  • "Single-Site Localization via Maximum Discrimination Multipath Fingerprinting," Jaffe, A.; Wax, M., Signal Processing, IEEE Transactions on , vol.62, no.7, pp.1718,1728, April1, 2014. (ID#:14-1298) Available at: A novel approach to single-site localization based on maximum discrimination multipath fingerprinting is presented. In contrast to the existing approach, which extracts each fingerprint only from the data of that location, the new approach uses also the data of all the other locations in the database, and leverages it to extract a fingerprint that is as different as possible from the other fingerprints in the database. The performance of this approach, validated with both simulated and real data, is superior to the existing approach, demonstrating single-site localization accuracy of 1 m in typical indoor environments. The new approach has also a lower computational complexity.


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.