Impulsive Interference Environments Research Articles

Robust equalization for single-carrier underwater acoustic communication in sparse impulsive interference environment

Impulse noise significantly distorts the background noise within communication systems, causing a notable departure from the Gaussian distribution. This distortion, in turn, exerts a detrimental influence on the performance of conventional single-carrier frequency domain equalization techniques. In this paper, we introduce a novel algorithm for joint channel, interference, and symbol estimation within single-carrier systems. This algorithm is grounded in the framework of variational Bayesian inference and possesses the unique capability of simultaneously accounting for the sparsity inherent in the channel and impulse interference. In the course of processing, it leverages multi-parameter probability distributions to effectively model the intricate posteriori probabilities associated with impulse interference vectors and channel vectors. This, in turn, facilitates the computation of maximum a posteriori estimates and subsequently enables the estimation and elimination of interference. We validate the efficacy of our proposed method through both numerical simulations and the processing of experimental data. Our results unequivocally demonstrate the robustness and manageable computational complexity of our approach within the context of impulse interference-prone environments.

Robust Subband Adaptive Filter Algorithms-Based Mixture Correntropy and Application to Acoustic Echo Cancellation

To acquire an improvement of the performance of the subband adaptive filter with impulsive interference, the normalized subband adaptive filter (NSAF) algorithm-based maximum correntropy criterion (MCC), called MCC-NSAF, has been developed. However, it is hard for the MCC criterion to take into account both the fast convergence rate and low steady-state error with a fixed kernel bandwidth. To handle this dilemma, in this paper, the normalized subband adaptive filter algorithm-based maximum mixture correntropy criterion (MMC) algorithm, called MMC-NSAF, is proposed. The MMC-NSAF algorithm integrates two correntropies with complementary control sizes of Gaussian kernel and can not only converge quickly but also has a small misalignment. Yet the MMC-NSAF still has some limitations because of its fixed step size. Therefore, a convexly combined MMC-NSAF algorithm, namely MMC-CNSAF, is proposed. The MMC-CNSAF algorithm utilizes two Gaussian kernels of different size and can combine overall improvement between convergence rate and steady-state error simultaneously. Eventually, the convergence and the computational complexity are analyzed. To achieve the test and verification of the robustness of proposed algorithms, simulations are carried out to discuss the effect of the parameters algorithm under colored input signals in impulsive interference environments for system identification. Meanwhile, the performances of the proposed algorithms under the influence of the acoustic echo cancellation in practical application are studied.

A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Recently, the sparsity-aware sign subband adaptive filter algorithm with individual-weighting-factors (S-IWF-SSAF) was devised. To accomplish performance enhancement, the variable parameter S-IWF-SSAF (VP-S-IWF-SSAF) algorithm was developed through optimizing the step-size and penalty factor, respectively. Different from the optimization scheme, we devise a family of variable step-size strategy S-IWF-SSAF (VSS-S-IWF-SSAF) algorithms based on the transient model of algorithms via minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. And in order to enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. It is worth mentioning that the presented algorithms could acquire lower computational requirements and exhibit higher steady-state estimation accuracy obviously and acceptable tracking characteristic in comparison to the VP-S-IWF-SSAF algorithm. In addition, the stable step-size range in the mean and mean square sense and steady-state performance are concluded. And the computational requirements are exhibited as well. Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to other related algorithms within various system inputs under impulsive interference environments.

Diffusion adaptive filtering algorithm based on the Fair cost function

To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF algorithm yields improved robustness against impulsive interferences and lower steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L0 norm constraint SSS-NSAF (SL0-SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L0 norm constraint step-size scaler improved proportionate NSAF (SL0-SSS-IPNSAF), is derived by inserting the logarithm cost function into the sigmoid function structure as well as the L0 norm of the weight coefficient vector to act as a new cost function. Since the use of the fix step size in the proposed SL0-SSS-IPNSAF algorithm, it needs to make a trade-off between fast convergence rate and low steady-state error. Thus, the convex combination version of the SL0-SSS-IPNSAF (CSL0-SSS-IPNSAF) algorithm is proposed. Simulations in acoustic echo cancellation (AEC) scenario have justified the improved performance of these proposed algorithms in impulsive interference environments and even in the impulsive interference-free condition.

Affine-Projection Lorentzian Algorithm for Vehicle Hands-Free Echo Cancellation

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo cancellation in vehicle hands-free communication systems and video teleconferencing systems, namely the affine-projection Lorentzian (APL) algorithm. By minimizing the Lorentzian norm of the a posteriori error vector with a suitable constraint on the weight vector and providing a dynamic Lorentzian-norm-controlling parameter, the proposed APL algorithm achieves robustness against impulsive disturbances and speeds up convergence for colored input signals. The computational complexity of the APL algorithm is analyzed and a fast recursive filtering method is employed to reduce its complexity. The stability analysis, based on energy-conservation arguments, shows that the APL algorithm converges. Furthermore, its tracking behavior is also investigated and a step size optimizing the tracking performance is derived. Simulation results agree well with the theoretical analysis. Simulation results for channel estimation and in-car echo cancellation scenarios demonstrate that the APL algorithm achieves better performance compared to the maximum-correntropy-criterion, affine-projection-generalized-maximum-correntropy, affine-projection-sign, affine-projection-like M-estimate, and Lorentzian adaptive filtering algorithms in various impulsive interference environments.

Impulsive noise modeling and robust receiver design

Interference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the different statistical models proposed for such an interference, that can generally be unified using the class of sub-exponential family of distributions, and its impact on the receiver design. Visualizing the optimal decision boundaries allows one to show the non linear effect induced by impulsive noise models, which explains the significant loss in receiver performance designed under the standard Gaussian approximation. This motivates the need to develop new receivers. We propose a framework to design receivers robust to a variety of interference types, both Gaussian and non-Gaussian. We explore three ways of thinking about such receiver designs: a linear approach; by approximating the noise plus interference distribution; and by mimicking the decision rule distribution directly. Except for the linear approach, the other designs are capable of replicating the non-trivial optimal decision regions to different extents. The new detection algorithms are evaluated via Monte Carlo simulations. We focus on four efficient architectures, including the parameter estimations: Myriad, Normal Inverse Gaussian, p-norm and a direct estimation of the likelihood ratio function. They exhibit good performance, close to the optimal, in a large range of situations demonstrating they may be considered as robust decision rules in the presence of heavy tailed or impulsive interference environments.

Diffusion affine projection maximum correntropy criterion algorithm and its performance analysis

The convergence performance of the conventional diffusion affine projection algorithm (DAPA) will be affected in impulsive noise environment. To overcome this shortcoming, in this paper, a diffusion affine projection maximum correntropy criterion (DAPMCC) algorithm is derived by using the maximum correntropy criterion in the diffusion network. The convergence of the DAPMCC algorithm in the mean and mean-square sense is studied, providing a constrained range for the step-size. Meanwhile, the theoretical steady-state mean square analysis of the proposed algorithm shows that the steady-state mean square deviation (MSD) is determined by parameters such as step-size and projection order. Simulation results demonstrate that the DAPMCC algorithm shows desirable estimation performance under impulsive interference environment, and the theoretical steady-state analysis is able to provide an accurate prediction.

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

A sparsity-aware variable kernel width proportionate affine projection

Robust Diffusion Affine Projection M-Estimate Algorithm for Distributed Estimation over Network

In this paper, a robust diffusion affine projection M-estimate (DAPM) algorithm is proposed for distributed estimation in the adaptive diffusion network. To eliminate the adverse effects of impulsive noise in case of the impulsive interference environment on the filter weight updates, this algorithm uses a robust cost function based on M-estimate function and is derived by the steepest-descent method. Simulation results verify that the proposed DAPM algorithm is effective for system identification scenarios in the presence of impulsive noise.

Diffusion Sparse Sign Algorithm with Variable Step-Size

In this paper, we propose the diffusion sparse sign algorithm with variable step-size for distributed estimation in sparse and impulsive interference environments. Firstly, we address the problem of in-network distributed estimation for sparse vectors under the impulsive noise environment. In order to exploit the sparsity of the vector of interest, we incorporate the sparse norms ( $${l_1}$$ -norm and $$RW{l_1}$$ -norm) into the cost function of the standard diffusion sign algorithm, which accelerates the convergence speed of zero or near-zero components. In addition, we propose the adaptive variable step-size to further improve the convergence rate of the proposed algorithm. The variable step-size is derived by the correlation entropy, which contains a modified Gaussian kernel function and is robust to impulsive noise. In this paper, every node combines its correlation entropy function with the information of its neighborhood to drive the variable step-size at each iteration. Simulation results show that the proposed algorithm outperforms the standard diffusion SA in the sparse and impulsive system and the convergence rate of the proposed algorithm is faster than constant step-size algorithms.

Diffusion normalized Huber adaptive filtering algorithm

In this paper, we propose an adapt-then-combine (ATC) diffusion normalized Huber adaptive filtering (ATC-DNHuber) algorithm for distributed estimation in impulsive interference environments. Firstly, a normalized Huber adaptive filter (NHuber) is developed to reduce the effect of the eigenvalue spread of the input signal. Then we extend the NHuber to develop an ATC diffusion algorithm by applying the NHuber algorithm at all agents. In addition, the mean stability performance and computational complexity are analyzed theoretically. In addition, Furthermore, simulation results demonstrate that the ATC-DNHuber algorithm can perform better in identifying the unknown coefficients under the complex and changeable impulsive interference environments.

Delayless Individual-Weighting-Factors Sign Subband Adaptive Filter With Band-Dependent Variable Step-Sizes

Sign subband adaptive filter algorithms with individual weighting factors (IWF-SSAF) were recently proposed to achieve improved convergence performance in impulsive noise environments by fully utilizing the decorrelating property of subband adaptive filters. However, such sign subband adaptive filter (SSAF) algorithms may have an inherent signal path delay problem for real-time applications, and thus, can be restrictively applied. In this paper, a delayless IWF-SSAF with band-dependent variable step-sizes (BD-VSS), robust in impulsive noise environments, is proposed for real-time applications, whereby two delayless filter structures developed for the $\ell _2$ -norm-based SAF are employed along with the IWF-SSAF. In particular, a BD-VSS algorithm is also introduced for better convergence by applying the recent $\ell _1$ -norm minimization technique to respective subband. Finally, the performance of the proposed delayless IWF-SSAF with BD-VSS is verified in various impulsive interference environments such as system identification with impulsive noise, acoustic echo cancellation in a double-talk scenario, and active impulsive noise control.

Diffusion Sign Subband Adaptive Filtering Algorithm with Enlarged Cooperation and Its Variant

The recently proposed diffusion sign subband adaptive filtering (DSSAF) algorithm is more robust than most of mean-square error minimization criterion-based diffusion distributed estimation algorithms in an impulsive interference environment. To enhance its convergence rate and steady-state misalignment, this paper proposes a DSSAF algorithm with enlarged cooperation (DSSAF-EC). The DSSAF-EC algorithm exchanges not only the weight information but also measurements within individual neighborhoods. Moreover, a variant of the DSSAF-EC algorithm, called the proportionate DSSAF-EC (PDSSAF-EC) algorithm, is presented. It incorporates an adaptive gain matrix into the DSSAF-EC algorithm to proportionately adapt the weight vectors of agents. Simulation results verify that both the DSSAF-EC and PDSSAF-EC algorithms are robust against impulsive interference and that the PDSSAF-EC algorithm can obtain faster convergence rate than the DSSAF-EC algorithm in estimating a sparse unknown weight vector.

Angle/Doppler estimation in heavy-tailed clutter backgrounds

This work describes new methods on the modeling of the amplitude statistics of airborne radar clutter by means of alpha-stable distributions. We develop joint target angle and Doppler, maximum likelihood-based estimation techniques from radar measurements retrieved in the presence of impulsive uncorrelated noise modeled as an alpha-stable random process. We derive the Cramer-Rao bounds (CRBs) for the additive Cauchy interference scenario to assess the best case estimation accuracy which can be achieved. In addition, we introduce a new joint spatial- and Doppler-frequency high-resolution estimation technique based on the fractional lower order statistics of the measurements of a radar array. Simulation results demonstrate that the proposed methods can be of interest in the study of space-time adaptive processing (STAP) for airborne pulse Doppler radar arrays operating in impulsive interference environments.

Maximum likelihood localization of sources in noise modeled as a stable process

This paper introduces a new class of robust beamformers which perform optimally over a wide range of non-Gaussian additive noise environments. The maximum likelihood approach is used to estimate the bearing of multiple sources from a set of snapshots when the additive interference is impulsive in nature. The analysis is based on the assumption that the additive noise can be modeled as a complex symmetric /spl alpha/-stable (S/spl alpha/S) process. Transform-based approximations of the likelihood estimation are used for the general S/spl alpha/S class of distributions while the exact probability density function is used for the Cauchy case. It is shown that the Cauchy beamformer greatly outperforms the Gaussian beamformer in a wide variety of non-Gaussian noise environments, and performs comparably to the Gaussian beamformer when the additive noise is Gaussian. The Cramer-Rao bound for the estimation error variance is derived for the Cauchy case, and the robustness of the S/spl alpha/S beamformers in a wide range of impulsive interference environments is demonstrated via simulation experiments.

Adaptive algorithm for detection of signals in an impulsive interference environment

Impulsive interferences are frequently long relative to signal duration. This situation is observed, for example, in a telephone channel used for medium and high-speed data communication. In this paper a novel algorithm for signal detection under conditions of long impulsive interferences is proposed. The approach does not require a priori knowledge of the impulse noise statistics. It is based on the representation of the impulse noise using a linear prediction model. Performance of I he proposed algorithm is investigated using computer simulation. It is shown that the algorithm provides a greatly reduced error rate as compared with the conventional non-linear receiver. Its performance is virtually independent of the power of impulse noise.

Optimum reception in an impulsive interference environment (Ph.D. Thesis abstr.)

Optimum reception in an impulsive interference environment (Ph.D. Thesis abstr.)