Impulsive Noise Rejection Research Articles

Robust Adaptive Filter Algorithms Against Impulsive Noise

This paper proposes a prefiltered observation-based adaptive filter algorithm that is robust against impulsive noise. Previous impulsive noise rejection algorithms were based on output error stochastic, so there was a trade-off relationship between impulsive noise detection and tracking performances. The proposed rejection algorithm is derived by using the statistics of the observed signal and the inequality such as the Schwarz and Young inequality in the absence of impulsive noise. From this, the proposed algorithm updates the weight vector only when the observed signal is not corrupted by impulsive noise. The proposed algorithm achieves the good tracking performance because it distinguishes between the system change and interruption of impulsive noise. In addition, the proposed algorithm has same performance without impulsive noise, compared with the normalized least-mean-square-type algorithm. Further, the proposed rejection algorithm could expand to various adaptive filtering structures, which suffer the performance degradation with impulsive noise, because it is easy to implement. Hence, the proposed algorithm is combined with the NLMS algorithm for dispersive systems and the proportionate NLMS algorithm for sparse systems. Simulation results show that the proposed algorithm achieves fast convergence rate, good tracking performance, and robustness under the impulsive noise environment.

Impulsive noise rejection for ZigBee communication systems using Error-Balanced Wavelet filtering

The physical (PHY) layer of ZigBee communication systems was defined by IEEE 802.15.4 and has good external white noise resistance due to its spread spectrum characteristic and error correction of the baseband coding process. However, previous research has shown the performance of ZigBee to degrade in the presence of impulsive noise. In this regard, an improvement of the ZigBee receiver is warranted in order to improve the decoding process. A novel Error-Balanced Wavelet filtering approach utilizing the multiresolution property is proposed to suppress the impact of impulsive noise prior to symbol detection and thus improve the bit error rate (BER) performance of the ZigBee demodulation process. This assessment is based on computer simulations and verifies that the overall transmission performance is improved by our proposed approach. The results obtained are also compared with existing impulsive noise suppression approaches and it is shown that our wavelet-based method outperforms other methods in improving the system BER.

Impulsive noise rejection method for compressed measurement signal in compressed sensing

Abstract The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

Adaptive robust detection for CDMA using a genetic algorithm

A novel adaptive robust multi-user detector for code-division multiple access (CDMA) using a real-coded genetic algorithm (GA) is proposed. The GA implicitly implements Huber's M-estimator and is robust against heavy-tailed impulsive noise. The novel feature of this GA-based detector lies in joint symbol detection and adaptive estimation of the cut-off parameter of the M-estimator's object function through a GA optimisation strategy. In particular, the GA-based detector carries out a multipoint search by manipulating and maintaining a population of candidate solutions for different values of the cut-off parameter to encourage information formation and exchange. Since the GA treats the cut-off parameter as one of its optimisation parameters, the need for a separate channel estimator is thus eliminated. Simulation results are provided to examine the evolutionary behaviour and the detection performance of the proposed GA-based detector. It is shown that the GA approach provides good adaptive impulsive noise rejection capability.

Spectral performance characterizations of some generalized median filters

Output correlations and power spectral densities are obtained for two types of generalized median filters: the L- and M-filters, for a sequence of independent, identically distributed inputs and also for a simple first-order Markov input sequence. The results show the ability of generalized median filters to flexibly achieve performance compromises between edge preservation and impulsive noise rejection, and Gaussian noise suppression.