Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.

Impulsive Noise Characterization of In-Vehicle Power Line

Abstract: Impulsive noise can have a great influence on the performance of in-vehicle power line communication systems. Intensive noise measurements in the time domain were thus carried out on five different vehicles. Preliminary trials were first made on a stationary vehicle and the motor idling, but the characteristics of the measured low-amplitude pulses greatly vary from one car to another. We thus emphasize the characteristics of high-amplitude pulses, greater than 70 mV, observed when the vehicles were moving in traffic, during a 20-min trip. Noise is statistically characterized in terms of duration, frequency content, peak amplitude, and time interval between successive pulses. Stochastic models based on mathematical distribution functions and fitting the experimental distribution of the various pulse characteristics are proposed. It has been found that interarrival time, i.e., the time interval between two successive pulses, is rather short and would be thus the most critical parameter when optimizing the power line communication physical layer.

ℓ 0 TV: A new method for image restoration in the presence of impulse noise

Abstract: Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on TV for image restoration in the presence of impulse noise. This type of noise frequently arises in data acquisition and transmission due to many reasons, e.g. a faulty sensor or analog-to-digital converter errors. Removing this noise is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) [42], which is based on TV with l 02 -norm data fidelity, only give sub-optimal performance. In this paper, we propose a new method, called l 0 TV -PADMM, which solves the TV-based restoration problem with l 0 -norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our l 0 TV -PADMM method finds a desirable solution to the original l 0 -norm optimization problem and is proven to be convergent under mild conditions. We apply l 0 TV -PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that l 0 TV -PADMM outperforms state-of-the-art image restoration methods.

On diversity reception over fading channels with impulsive noise

Abstract: In this paper, we analyze the performance of different diversity combining techniques over fading channels with impulsive noise. We use Middleton's Class A model for the noise distribution and adopt two noise models, which assume dependent and independent noise components on each branch. We systematically analyze the performance of maximum ratio combing (MRC), equal gain combining (EGC), selection combining (SC), and post-detection combining (PDC) under these impulsive noise models, and derive insightful lower and upper bounds. We show that even under impulsive noise, the diversity order is retained for each combining scheme. However, we also show that under both models, there is a fundamental tradeoff between diversity gain and coding gain. Under the independent noise model, PDC is shown to combat impulsive noise more effectively than MRC, EGC, and SC. Our simulation results also corroborate our analysis.

Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information

Abstract: Commonly employed reconstruction algorithms in compressed sensing (CS) use the L2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vector leading to a poor performance when the noise no longer follows the Gaussian assumption but, instead, is better characterized by heavier-than-Gaussian tailed distributions. In this paper, we propose a robust iterative hard Thresholding (IHT) algorithm for reconstructing sparse signals in the presence of impulsive noise. To address this problem, we use a Lorentzian cost function instead of the L2 cost function employed by the traditional IHT algorithm. We also modify the algorithm to incorporate prior signal information in the recovery process. Specifically, we study the case of CS with partially known support. The proposed algorithm is a fast method with computational load comparable to the LS based IHT, whilst having the advantage of robustness against heavy-tailed impulsive noise. Sufficient conditions for stability are studied and a reconstruction error bound is derived. We also derive sufficient conditions for stable sparse signal recovery with partially known support. Theoretical analysis shows that including prior support information relaxes the conditions for successful reconstruction. Simulation results demonstrate that the Lorentzian-based IHT algorithm significantly outperform commonly employed sparse reconstruction techniques in impulsive environments, while providing comparable performance in less demanding, light-tailed environments. Numerical results also demonstrate that the partially known support inclusion improves the performance of the proposed algorithm, thereby requiring fewer samples to yield an approximate reconstruction.