Signal Compression Reconstruction with Narrow-Band Interference

2014 ◽  
Vol 668-669 ◽  
pp. 1110-1113
Author(s):  
Yong Jie Li ◽  
Dong Jiao Xu ◽  
Xin Wang ◽  
Ying Chang
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Computer Simulations ◽  
Narrow Band ◽  
Signal Compression ◽  
Processing Scheme ◽  
Reconstruction Performance

Compressive sensing (CS) implements sampling and compression to sparse or compressible signals simultaneously. Compressive signal processing is a new signal processing scheme base on compressive sensing theory. In this paper, the problem of signal compressive reconstruction base on narrow-band interference is researched. The reconstruction performance of BP, MP, and OMP algorithms with narrow-band interference is analyzed by computer simulations.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Low coherence, interferometric fibre optic vibrometer using novel optical signal processing scheme

1991 ◽  
Vol 27 (18) ◽  
pp. 1658 ◽  
Author(s):  
K. Weir ◽  
W.J.O. Boyle ◽  
A.W. Palmer ◽  
K.T.V. Grattan ◽  
B.T. Meggitt
Keyword(s):  
Signal Processing ◽  
Optical Signal Processing ◽  
Optical Signal ◽  
Fibre Optic ◽  
Processing Scheme ◽  
Low Coherence

A High Efficient Baseband GNSS Signal Narrow Band Anti-Jamming Approach in Frequency Domain

2014 ◽  
Vol 926-930 ◽  
pp. 1857-1860
Author(s):  
Zhou Zheng ◽  
Meng Yuan Li ◽  
Wei Jiang Wang
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Narrow Band ◽  
Low Frequency ◽  
Frequency Resolution ◽  
Interference Rejection ◽  
High Efficient ◽  
Digital Down Conversion ◽  
Down Conversion ◽  
Low Rate

In order to reduce the burden of the calculation and the low frequency resolution of the tradition GNSS signal intermediate narrow band anti-jamming method, it introduces a high efficient approach of narrow band interference rejection based on baseband GNSS signal processing. After digital down conversion to baseband and down sampling to a low rate, the interference is removed in frequency domain. According to the theoretical analysis and simulation, it claims that the method can reduce the calculation and increase the detection resolution in frequency domain which will realize a high efficient interference rejection.

scholarly journals Combination of HP and LP filters for narrow band antialiasing filter

2021 ◽  
Vol 72 (4) ◽  
pp. 283-286
Author(s):  
Bohumil Brtník
Keyword(s):  
Signal Processing ◽  
Discrete Time ◽  
Operational Amplifier ◽  
Narrow Band ◽  
Time Signal ◽  
Spice Simulation ◽  
Output Resistance ◽  
High Frequencies ◽  
Filter Structure ◽  
Reconstruction Filter

Abstract The discrete time signal processing requires an anti-aliasing filter at the input and a reconstruction filter at output. Some filters of biquads structure are characterized by a decreasing of the attenuation at high frequencies, caused by the final value of the output resistance of the operational amplifier. In this paper we discuss a design of combined BP filter without mentioned decrease. The proposed filter structure was verified by SPICE simulation.

scholarly journals Validation of a new signal processing scheme for the MST radar at Aberystwyth

2008 ◽  
Vol 26 (11) ◽  
pp. 3253-3268 ◽  
Author(s):  
D. A. Hooper ◽  
J. Nash ◽  
T. Oakley ◽  
M. Turp
Keyword(s):  
Signal Processing ◽  
Measurement Error ◽  
Gravity Wave ◽  
Wave Activity ◽  
Horizontal Wind ◽  
Time Averaging ◽  
Random Measurement Error ◽  
Processing Scheme ◽  
Random Measurement ◽  
Short Period

Abstract. This paper describes a new signal processing scheme for the 46.5 MHz Doppler Beam Swinging wind-profiling radar at Aberystwyth, in the UK. Although the techniques used are similar to those already described in literature – i.e. the identification of multiple signal components within each spectrum and the use of radial- and time-continuity algorithms for quality-control purposes – it is shown that they must be adapted for the specific meteorological environment above Aberystwyth. In particular they need to take into account the three primary causes of unwanted signals: ground clutter, interference, and Rayleigh scatter from hydrometeors under stratiform precipitation conditions. Attention is also paid to the fact that short-period gravity-wave activity can lead to an invalidation of the fundamental assumption of the wind field remaining stationary over the temporal and spatial scales encompassed by a cycle of observation. Methods of identifying and accounting for such conditions are described. The random measurement error associated with horizontal wind components is estimated to be 3.0–4.0 m s−1 for single cycle data. This reduces to 2.0–3.0 m s−1 for data averaged over 30 min. The random measurement error associated with vertical wind components is estimated to be 0.2–0.3 m s−1. This cannot be reduced by time-averaging as significant natural variability is expected over intervals of just a few minutes under conditions of short-period gravity-wave activity.

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