Adaptive robust impulse noise filtering (original) (raw)

A robust detector for impulsive noise environment

2007

This paper proposes a robust detector for detection of known signals in impulsive noise environment. The impulsive noise is assumed to be present in addition to the usual additive white Gaussian noise and is modeled as a uniformly distributed random variable that appears with a certain probability. In the paper the detector for the aforementioned noise model is derived and its performance is investigated. It is shown that the detector outperforms the usual matched filter detector in case the impulsive noise is present while the performance is similar to that of matched filter in absence of the impulsive noise.

A normalized mixed-norm adaptive filtering algorithm robust under impulsive noise interference

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).

A Normalized Robust Mixed-Norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard Robust Mixed-Norm (RMN) algorithm, despite its ability to cope with impulsive noise by virtue of combining the first and second error norm in the cost function it minimizes, exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size which needs to be determined a-priori. To overcome these limitations, the proposed NRMN algorithm introduces a time varying learning rate which is derived based upon the dynamics of the input signal, and thus no longer requires a stationary environment, a major drawback of the RMN algorithm. The normalized step-size is bounded from above and a parameter is introduced within its upper-bound, which provides a trade-off between the convergence rate and the steady-state coefficient error. The analysis and experimental results show that the proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the Least Absolute Deviation (LAD) and RMN algorithm.

A low complexity robust detector in impulsive noise

Signal Processing, 2009

This paper demonstrates the effectiveness of a nonlinear extension to the matched filter for signal detection in certain kinds of non-Gaussian noise. The decision statistic is based on a new measure of similarity that can be considered as an extension of the correlation statistic used in the matched filter. The optimality of the matched filter is predicated on second order statistics and hence leaves room for improvement, especially when the assumption of Gaussianity is not applicable. The proposed method incorporates higher order moments in the decision statistic and shows an improvement in the receiver operating characteristics (ROC) for non-Gaussian noise, in particular, those that are impulsive distributed. The performance of the proposed method is demonstrated for detection in two types of widely used impulsive noise models, the alpha-stable model and the two-term Gaussian mixture model. Moreover, unlike other kernel based approaches, and those using the characteristic functions directly, this method is still computationally tractable and can easily be implemented in real-time.

A new LMS/newton algorithm for robust adaptive filtering in impulsive noise

2004

ABSTRACT This paper proposes a new LMS/Newton algorithm for robust adaptive filtering in impulse noise. The new algorithm is obtained by applying the non-linear filtering technique and the robust statistic approach to the conventional fast LMS/Newton method. A robust method for estimating the required threshold parameters for impulse suppression is also given.

Robust estimation methods for impulsive noise suppression in speech

…, 2005

Abstract— We discuss a new robust time domain filtering method that detects and reconstructs speech segments corrupted by impulsive noise. Robust statistical methods are very effective in the case of impulsive environments such as wireless communications and cellular ...

Active noise control of impulsive noise with selective outlier elimination

2013 American Control Conference, 2013

Traditional active noise control (ANC) methods are based on adaptive filtering algorithms designed to minimize the noise variance. The convergence of such algorithms may be jeopardized in the presence of non-Gaussian noise signals, characterized by a marked impulsiveness (and infinite secondorder moments), such as are frequently encountered in realworld acoustic settings. ANC methods have been recently extended to deal with such signals, modifying the weight update of the adaptive filter so that out-of-range samples are discarded or discounted. These methods require precise a priori knowledge of the impulsive characteristics of the noise and are generally not suitable for signals where such characteristics are timevarying. This work introduces an algorithm, based on an adaptive box-plot approach for outlier detection, which does not rely on any a priori information and yields uniformly high attenuation performance in all conditions tested in simulation.

Practical Implementation of Adaptive Analog Nonlinear Filtering for Impulsive Noise Mitigation

2018 IEEE International Conference on Communications (ICC), 2018

It is well known that the performance of OFDMbased Powerline Communication (PLC) systems is impacted by impulsive noise. In this work, we propose a practical blind adaptive analog nonlinear filter to efficiently detect and mitigate impulsive noise. Specially, we design an Adaptive Canonical Differential Limiter (ACDL) which is constructed from a Clipped Mean Tracking Filter (CMTF) and Quartile Tracking Filters (QTFs). The QTFs help to determine a real-time range that excludes outliers. This range is fed into the CMTF which is responsible for mitigating impulsive noise. The CMTF is a nonlinear analog filter and its nonlinearity is controlled by the aforementioned range. Proper selection of this range ensures the improvement of the desired signal quality in impulsive environment. It is important to note that the proposed ACDL behaves like a linear filter in case of no impulsive noise. In this context, the traditional matched filter construction is modified to ensure distortionless processing of the desired signal. The performance improvement of the proposed ACDL is due to the fact that unlike other nonlinear methods, the ACDL is implemented in the analog domain where the outliers are still broadband and distinguishable. Simulation results in PRIME (OFDM-based narrowband PLC system) demonstrate the superior BER performance of ACDL relative to other nonlinear approaches such as blanking and clipping in impulsive noise environments. Index Terms-Impulsive noise, analog nonlinear filter, adaptive canonical differential limiter (ACDL), clipped mean tracking filter (CMTF); quantile tracking filter (QTF), orthogonal frequency-division multiplexing (OFDM), powerline communication (PLC).

One dimensional nonlinear adaptive filters for impulse noise suppression

Proceedings of the 5th Wseas International Conference on Applications of Electrical Engineering, 2006

An adaptive filter is essentially a digital filter with self-adjusting characteristic. It adapts, automatically, to changes in its input signals. The contamination of a signal of interest by other unwanted, often lager, signals or noise is a problem often encountered in many applications. Typical applications where adaptive filters are appropriate are the following: Digital communication using a spread spectrum, where a large jamming signal, possibly intended to disrupt communication, could interfere with the desired signal. The interference often occupies a narrow but unknown band within the wideband spectrum, and can only be effectively dealt with adaptively. Digital data communication over the telephone channel at the high data rate. Adaptive algorithms are used to adjust the coefficients of the digital filter such that error signal is minimized according to some criterion, for example in the least squares sense. The Nonlinear Normalized Mean Square algorithm is applicable to a wide variety of nonlinear filters. In this paper, algorithms are developed for an optimal time-varying step-size for FIR, Volterra, weighted median and weighted myriad filters.

Use of adaptive algorithm for impulsive noise cancellation

International Journal of Internet of Things and Cyber-Assurance, 2018

Desire of clean signal at user end is a great demand. Adaptive algorithms are most suitable for such task. In this paper, the authors have taken an attempt for synthetic signal contaminated with impulsive noise. Further, its application has been extended to noisy biomedical signal as ECG. It is very important to eliminate noise from the biomedical signal, as its occurrence is sudden and often similar to the signal. The popular adaptive algorithms have been used for cancellation of impulsive noise. Though different algorithms have been applied earlier, the novelty in this work is application of Wilcoxon LMS for impulsive noise case. Also, it has been modified for the same purpose. The result found excellent in terms of less MSE, SNR improvement and faster convergence.