A novel direction finding algorithm for distributed sources under impulsive noise environments

In this letter, a novel robust two-dimensional nearfield millimeter-wave (MMW) image reconstruction algorithm is developed based on compressed sensing (CS) in the presence of impulsive measurement noise. To enable the sparse MMW image recovery, the CS algorithm usually employs the popular l 2 -norm as the data fidelity term and a combination of multiple sparsityinduced functions as the penalty term. The l 2 -norm is non-robust against impulsive noise since the Gaussian noise distribution assumption is not valid, and the presence of impulsive noise will severely degrade the robustness of the compressive MMW image recovery. To gain more robustness performance, a complex correntropy based data-fitting term, namely complex correntropic loss (CC-loss), is used to replace the l2-norm for the near-field MMW measurement contaminated by impulsive noise. In order to solve the corresponding minimization problem, an additive half quadratic method is used to transform the CC-loss term to its convex form, and then a parallel primal-dual process is used to guarantee the convergence of the proposed algorithm. In total, 150-GHz MMW experimental results show that the proposed algorithm can achieve accurate image reconstruction from compressive measurements under different impulsive noise levels.

The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean square \((p=2)\), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the \(l_0\)-norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the \(l_1\)-norm, reweighted \(l_1\)-norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the \(l_0\)-norm.

Impulse noise is a potential impairment of very-high speed digital subscriber line (VDSL) services. VDSL systems are protected from impulse noise using a combination of Reed-Solomon coding and interleaving. Since there are two competing proposals for VDSL, the first one based on the use of QAM line code and the second one using DMT modulation, it is interesting to compare their performance in terms of immunity to impulse noise. The paper presents a comparison of the behavior of QAM-VDSL and DMT-VDSL in the presence of impulse noise in real configurations. The required interleaving delay to correct all the generated errors for both systems is investigated. It is shown that when no notches are used in the radio-amateur bands, QAM and DMT systems require similar delays in order to achieve the same level of protection against impulse noise. However, when notches are implemented, DMT-VDSL achieves higher rates than QAM-VDSL. Therefore, larger error-correcting code redundancy can be used, which results in lower end-to-end delays for DMT-VDSL systems.

One of the challenges of modulation techniques used in Fifth-Generation (5G) is their robustness in noisy environment. Conventional Orthogonal Frequency Division Multiplexing (OFDM) cannot be considered as a 5G waveform in its original form because of its certain limitations, such as performance degradation by impulsive noise (IN) and high peak to average power ratio (PAPR). Numerous modulation schemes proposed for 5G communications are able to overcome these drawbacks. Generalised Frequency Division Multiplexing (GFDM) is one of them. This paper analyses the performance of GFDM in presence of Additive White Gaussian Noise (AWGN), IN and Narrow Band Interference (NBI). It is found that GFDM is able to perform better than OFDM and Vector Orthogonal Frequency Division Multiplexing (VOFDM) in presence of noises, which can potentially be present in 5G applications. Simulation results show that GFDM achieve lower PAPR and Symbol Error Rate (SER) and an average of 10.73 dB and 4.73 dB gain in Signal to Noise Ratio (SNR) in presence of IN and combined IN and NBI respectively, as compared to OFDM and VOFDM.

An improved method for direction-of-arrival (DOA) estimation algorithm based on Multistage Wiener Filters (MSWF) in the presence of impulsive noise is proposed in this paper. There are many DOA estimation algorithms based on Multistage Wiener Filters, but all of them must satisfy the requirement of the noise is Gaussian white noise, which can not the accurately in the presence of impulsive noise. The improved method has good performance at DOA estimate when the noise is impulsive noise, and some computer simulations show it.

This paper studies the estimation of fast fading channel in the presence of impulse noise. The fast fading channel in a wireless communications system is typically modeled as an autoregressive (AR) process. The least-squares algorithm and Kalman filter have been previously proposed for estimating the AR parameters and the channel impulse response respectively using a training sequence. The performance of these algorithms, however, is very sensitive to impulse noise. In this paper, a robust Kalman filter and a robust recursive least M-estimate algorithm are employed to jointly estimate the channel impulse response and the AR parameters of fast fading channel under impulse noise. Simulation showed that the proposed algorithms are much less sensitive to impulse noise than the conventional algorithms.

The direct sequence-code division multiple accesses (DS-CDMA) signals are transmitted over multipath channels that introduce fading and shadowing. Combined effect of multipath fading and shadowing along with multiple access interference (MAI) and inter-symbol interference (ISI) worsens the system performance. Further, experimental results have confirmed the presence of impulsive noise in wireless mobile communication channels. Hence, this paper presents a comprehensive learning particle swarm optimization (CLPSO) based multiuser detection technique for DS-CDMA systems over generalized-K (GK) fading channels in presence of impulsive noise. Maximal ratio combining (MRC) receive diversity is also incorporated to mitigate the effects of fading and shadowing. Performance of proposed M-decorrelator is studied by evaluating average error rate. Simulation results show that the proposed M-decorrelator performs better in the presence of fading, shadowing and heavy-tailed impulsive noise when compared to least squares, Huber and Hampel M-estimator based detectors.

We address the distributed estimation of a scalar Gaussian source in wireless sensor networks. The sensor nodes transmit their noisy observations, using the amplify-and-forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario.

A simple method of improving orthogonal frequency division multiplexing (OFDM) receiver performance in an impulsive noise environment is to precede a conventional OFDM demodulator with blanking nonlinearity. This method is widely used in practice since it is efficient and very simple to implement. However, performance analysis of this scheme has not yet appeared. In this paper, we study performance of the OFDM receiver with blanking nonlinearity in the presence of impulsive noise. Closed form analytical expressions for the signal-to-noise ratio (SNR) at the output of blanking nonlinearity and the optimal blanking threshold that maximizes SNR are derived. Simulation results are provided that show good agreement with theory if the number of OFDM subcarriers is sufficiently large.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

Direction of arrival (DOA) estimation has emerged as a promising technology for many military and civilian applications, such as radio astronomy and wireless communications. However, it remains very challenging to estimate DOA for distributed sources within impulsive noise environments. As is widely known, the utilization of the subspace technology in array signal processing can enable high-resolution DOA estimation. In many instances, most of these algorithms assume that the noise is Gaussian. Considering the impulsive noise environments, the derivative of the error function is derived as the iteratively weighted factor in the objective function of low-rank decomposition. Two iterative algorithms are proposed to compute the signal subspace to implement DOA estimation for coherently distributed sources. To further improve the DOA estimation accuracy and increase the number of estimated sources, the virtual data samples are reconstructed from the extended co-prime array. Simulation results confirm that the two proposed impulsive noise suppression algorithms provide better performance than the existing algorithms. The virtual data samples that are reconstructed from the extended co-prime array can be utilized as the effective inputs for subspace-like algorithms.

In the context of two-dimensional (2-D) direction of arrival (DOA) estimation in impulsive noise environments, this paper extends the coprime array to a crossed V-shaped distributed structure and proposes a novel exponential weighted adaptive generalized covariance (EWAGC) based on the study of impulsive noise characteristics. Building on this, a redundant averaging dual-scale EWAGC-ESPRIT algorithm is introduced, which can adaptively adjust the suppression level of impulsive noise with varying intensities, enhancing the robustness of the estimation. Simulation results validate the superior performance of the proposed method in solving the 2-D DOA estimation problem in impulsive noise environments.

Generalized [formula omitted] minimization based DOA estimation for sources with known waveforms in impulsive noise

Robust Bayesian learning approach for massive MIMO channel estimation

A robust direction-of-arrival estimation method for impulsive noise environments