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Telfor Journal
2019, vol. 11, br. 1, str. 35-40
jezik rada: engleski
vrsta rada: neklasifikovan

Analysis of noise in complex-valued binary and bipolar sigmoid compressive sensing
(naslov ne postoji na srpskom)
aUniversity of Montenegro, Faculty of Electrical Engineering, Podgorica, Montenegro + University of Grenoble Alpes, INP Grenoble, GIPSA Lab, Grenoble, France
bUniversity of Montenegro, Faculty of Electrical Engineering, Podgorica, Montenegro



(ne postoji na srpskom)
Binary compressive sensing (CS) is a relatively new idea in the theory of sparse signal reconstruction. Under this framework, the signal is reconstructed based on the sign of the available measurements. This paper analyzes basic onebit CS concepts for the case of complex valued random Gaussian measurement matrices. The reconstruction is compared with the B-bit quantized measurements. The concept of binary CS-based reconstruction is generalized by applying a sigmoid function to the measurements. Noise influence is also considered. The reconstruction is performed using a simple iterative thresholding algorithm.

Ključne reči

compressive sensing; complex; binary; bipolar; sigmoid; reconstruction; sparse signal processing


Baraniuk, R. (2007) Compressive sensing. IEEE Signal Processing Magazine, 24(4): 118-121
Blumensath, T. (2011) Sampling and Reconstructing Signals from a Union of Linear Subspaces. IEEE Transactions on Information Theory, 57(7): 4660-4671
Blumensath, T., Davies, M.E. (2009) Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 27(3): 265-274
Boufounos, P.T. (2009) Greedy sparse signal reconstruction from sign measurements. u: 2009 Conference Record of the 43rd Asilomar Conference on Signals, Systems and Computers, CA, USA
Boufounos, P.T., Baraniuk, R.G. (2008) 1-Bit compressive sensing. u: 42nd Annual Conference on Information Sciences and Systems, Princeton, NJ, USA
Candes, E.J., Romberg, J., Tao, T. (2006) Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2): 489-509
Chen, C., Wu, J. (2015) Amplitude-Aided 1-Bit Compressive Sensing over Noisy Wireless Sensor Networks. IEEE Wireless Communications Letters, 4(5): 473-476
Dai, D., Shen, L., Xu, Y., Zhang, N. (2016) Noisy 1-bit compressive sensing: Models and algorithms. Applied and Computational Harmonic Analysis, 40(1): 1-32
Davenport, M.A., Duarte, M.F., Eldar, Y.C., Kutyniok, G. (2012) Introduction to compressed sensing. u: Compressed Sensing: Theory and Applications, Cambridge University Press, 1-64
Donoho, D.L. (2006) Compressed sensing. IEEE Transactions on Information Theory, 52(4): 1289-1306
Han, X., Yang, H., Huang, X. (2018) Review on One-Bit Compressive Sensing and its Biomedical Applications. u: 23rd International Conference on Digital Signal Processing (IEEE DSP), Shanghai, China, November
Huang, X., Yang, H., Huang, Y., Shi, L., He, F., Maier, A., Yan, M. (2019) Robust mixed one-bit compressive sensing. Signal Processing, 162: 161-168
Jacques, L., Laska, J.N., Boufounos, P.T., Baraniuk, R.G. (2011) Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors. IEEE Transactions on Information Theory, 59(4): 2082-2102
Li, Z., Xu, W., Zhang, X., Lin, J. (2018) A survey on one-bit compressed sensing: Theory and applications. Frontiers of Computer Science, 12(2): 217-230
Needell, D., Tropp, J.A. (2009) CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 26(3): 301-321
North, P., Needell, D. (2015) One-Bit Compressive Sensing with Partial Support. u: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), CMC Senior Theses, paper 1194, [Available online:
Stankovic, I., Brajovic, M., Dakovic, M., Stankovic, L. (2018) Complex-Valued Binary Compressive Sensing. u: 26th Telecommunications Forum (TELFOR 2018), November 20-21, 2018, Belgrade, Serbia
Stankovic, L., Stankovic, S., Amin, M. (2014) Missing samples analysis in signals for applications to L-estimation and compressive sensing. Signal Processing, 94: 401-408
Stankovic, L., Dakovic, M., Stankovic, I., Vujovic, S. (2017) On the Errors in Randomly Sampled Nonsparse Signals Reconstructed with a Sparsity Assumption. IEEE Geoscience and Remote Sensing Letters, 14(12): 2453-2456
Stanković, L. (2015) Digital Signal Processing with Selected Topics. CreateSpace Independent Publishing Platform: An Company, November 4
Stanković, L., Sejdić, E., Stanković, S., Daković, M., Orović, I. (2018) A Tutorial on Sparse Signal Reconstruction and Its Applications in Signal Processing. Circuits, Systems, and Signal Processing, 38(3): 1206-1263
Stanković, L., Daković, M. (2015) On the Uniqueness of the Sparse Signals Reconstruction Based on the Missing Samples Variation Analysis. Mathematical Problems in Engineering, 2015: 1-14
Stanković, S., Stanković, L., Orović, I. (2014) Relationship between the robust statistics theory and sparse compressive sensed signals reconstruction. IET Signal Processing, 8(3): 223-229
Stockle, C., Munir, J., Mezghani, A., Nossek, J.A. (2015) 1-bit direction of arrival estimation based on Compressed Sensing. u: IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2015, Stockholm, 246-250
Zayyani, H., Korki, M., Marvasti, F. (2016) A Distributed 1-bit Compressed Sensing Algorithm Robust to Impulsive Noise. IEEE Communications Letters, 20(6): 1132-1135