Blind parameter estimation of M-FSK signals in the presence of alpha-stable noise

Blind estimation of parameters for M-ary frequency-shift-keying (M-FSK) signals is great of importance in intelligent receivers. Many existing algorithms have assumed white Gaussian noise. However, their performance severely degrades when grossly corrupted data, i.e., outliers, exist. This paper solves this issue by developing a novel approach for parameter estimation of M-FSK signals in the presence of alpha-stable noise. Specifically, the proposed method exploits the generalized first- and second-order cyclostationarity of M-FSK signals with alpha-stable noise, which results in closed-form solutions for unknown parameters in both time and frequency domains. As a merit, it is computationally efficient and thus can be used for signal preprocessing, symbol timing estimation, signal and noise power estimation. Furthermore, substantial theoretical analysis on the performance of the proposed approach is provided. Simulations demonstrate that the proposed method is robust to alpha-stable noise and that it outperforms the state-of-the-art algorithms in many challenging scenarios

Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Thode, A., Wright, D., & Chapman, R. Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Society of America, 147(3), (2020): 1897, doi:10.1121/10.0000937.Classical ocean acoustic experiments involve the use of synchronized arrays of sensors. However, the need to cover large areas and/or the use of small robotic platforms has evoked interest in single-hydrophone processing methods for localizing a source or characterizing the propagation environment. One such processing method is “warping,” a non-linear, physics-based signal processing tool dedicated to decomposing multipath features of low-frequency transient signals (frequency f  1 km). Since its introduction to the underwater acoustics community in 2010, warping has been adopted in the ocean acoustics literature, mostly as a pre-processing method for single receiver geoacoustic inversion. Warping also has potential applications in other specialties, including bioacoustics; however, the technique can be daunting to many potential users unfamiliar with its intricacies. Consequently, this tutorial article covers basic warping theory, presents simulation examples, and provides practical experimental strategies. Accompanying supplementary material provides matlab code and simulated and experimental datasets for easy implementation of warping on both impulsive and frequency-modulated signals from both biotic and man-made sources. This combined material should provide interested readers with user-friendly resources for implementing warping methods into their own research.This work was supported by the Office of Naval Research (Task Force Ocean, project N00014-19-1-2627) and by the North Pacific Research Board (project 1810). Original warping developments were supported by the French Delegation Generale de l'Armement

Doppler spread estimation in mobile fading channels

The Doppler spread, or equivalently, the mobile speed, is a measure of the spectral dispersion of a mobile fading channel. Accurate estimation of the mobile speed is important in wireless mobile applications which require such as knowledge of the rate of channel variations. In this dissertation, first the performance of classical crossing- and covariance-based speed estimators is studied. Next, the problem of mobile speed estimation using diversity combining is investigated. Then, a nonparametric estimation technique is proposed that is robust to different channel variations. Finally, cyclostationarity-based speed estimators which can be applied either blindly or with the aid of pilot data, are developed. A unified framework for the performance analysis of well-known crossing and covariance based speed estimation techniques is presented. This allows a fair analytical comparison among all the methods. Interestingly, it is proved that all these methods are asymptotically equivalent, i.e., for large observation intervals. The extensive performance analysis, supported by Monte Carlo simulations, has revealed that depending on the channel condition and the observation interval, one needs to use a crossing or a covariance based technique to achieve the desired estimation accuracy over a large range of mobile speeds. Two common diversity schemes, selection combining (SC) and maximal ratio combining (MRC), are considered for Doppler spread estimation. Four new estimators are derived which rely on the inphase zero crossing rate, inphase rate of maxima, phase zero crossing rate, and the instantaneous frequency zero crossing rate of the output of SC. Two estimators, which work based on the level crossing rates of the envelopes at the output of SC and MRC, are also proposed. The performances of all these estimators are investigated in realistic noisy environments with different kinds of scatterings and different numbers of diversity branches. Then a novel speed estimation technique is proposed that is applicable to both mobile and base stations, based on the characteristics in the power spectrum of mobile fading channels. The analytic performance analysis, verified by Monte Carlo simulations, shows that this low-complexity estimator is not only robust to both Gaussian and non-Gaussian noises, but also insensitive to nonisotropic scattering observed at the mobile. The estimator performs very well in both two- and three-dimensional propagation environments. By taking advantage of resolvable paths in wideband fading channels, the robustness against both nonisotropic scattering and line of sight can be further increased, due to the differences among the Doppler spectra observed at different paths. This technique is also extended to base stations with antenna arrays. By exploiting the spatial information, the proposed space-time estimator exhibits excellent performance over a wide range of noise power, nonisotropic scattering, and the line-of-sight component. This is all verified by simulation. The utility of the new method is further demonstrated by applying it to the measured data. Finally, to design robust blind and data-aided mobile speed estimators, a proposal is made to exploit the inherent cyclostationarity of linearly modulated signals transmitted through fading channels. Two categories of cyclic-correlation- and cyclic-spectrum-based methods are developed. Extension to space-time speed estimation at the base station in macrocells is also provided. In comparison with the existing methods, the new estimators can be used without any need for pilot tones and are robust to additive stationary noise or interference of any color or distribution. Unlike the conventional multi-antenna based method, the proposed space-time speed estimator does not assume the receiver noise to be spatially white. A suboptimal training sequence is also devised for pilot-symbol assisted methods, to reduce the estimation error. The performance of the proposed estimators are illustrated via extensive Monte Carlo simulations

Automatic classification of digital communication signal modulations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityAutomatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fuelled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature selection and combination. We have also developed a new distribution test based classifier which is tailored for modulation classification with the inspiration from Kolmogorov-Smirnov test. The proposed classifier is shown to have improved accuracy and robustness over the standard distribution test. For blind classification in imperfect channels, we developed the combination of minimum distance centroid estimator and non-parametric likelihood function for blind modulation classification without the prior knowledge on channel noise. The centroid estimator provides joint estimation of channel gain and carrier phase o set where both can be compensated in the following nonparametric likelihood function. The non-parametric likelihood function, in the meantime, provide likelihood evaluation without a specifically assumed noise model. The combination has shown to have higher robustness when different noise types are considered. To push modulation classification techniques into a more timely setting, we also developed the principle for blind classification in MIMO systems. The classification is achieved through expectation maximization channel estimation and likelihood based classification. Early results have shown bright prospect for the method while more work is needed to further optimize the method and to provide a more thorough validation.School of Engineering and Design Brunel University London, the Faculty of Engineering University of Liverpool, and the University of Liverpool Graduate Association (Hong Kong)