It is well known that in high-speed movements, the positioning accuracy of global positioning system (GPS) receivers decreases drastically. The models presented so far to describe high-speed motion do not represent the state of the system precisely, so positioning accuracy with the methods based on these models is not appropriate. Here, a comprehensive method is proposed to solve the accuracy issue of the single-frequency GPS receiver at high-speed motions without increasing the computational complexity. Suitable modelling of the GPS receivers at high-speed motion, using the sequential extended Kalman filter, correct determination of the process noise covariance matrix and accurately estimating the variance of the observations are the basics of the proposed approach. Simulations with different data and motion scenarios (at speeds from 100 to 7300 m/s) show that the proposed method, while not increasing the computational cost, improves the accuracy of positioning more than 70% when compared to the conventional methods.

Abstract A design technique is developed for the probing signals of a Multi‐Input Multi‐Output (MIMO) radar. The concentration of the energy of the signal in its essential duration and essential bandwidth is achieved through the use of a class of time‐frequency concentrated functions called the WLJ functions as the synthesizing signal set. The goal is to design a signal vector having a pre‐specified desired covariance (CoV) matrix while ensuring that the side‐lobes of the ambiguity functions are small. Since CoV matrices are structurally constrained, they form a manifold in the signal space. Hence, we argue that the difference between these matrices should not be measured in terms of the conventional Euclidean distance (ED); rather, the distance should be measured along the surface of the manifold, that is, in terms of a Riemannian distance (RD). In either case, the signal optimisation problem is non‐convex in the design variables, involving, respectively, a quartic and a square‐root objective function. An efficient algorithm based on successive convex approximation is developed in which the original non‐convex problems are transformed so that they can be approximated by a convex quadratically constrained quadratic problem at each stage, resulting in good approximate solutions. Comparing the designs using ED and RD, we find that the convergence of the algorithm can be significantly faster when optimising over the manifold (RD) than when optimising over the whole space (ED). More importantly, for tight constraints, the use of RD yields solutions which satisfy the constraints far better than the use of ED.

Abstract A mixed near‐field and far‐field targets localization method based on sparse signal reconstruction and subspace method is presented, which can obtain both direction‐of‐arrival (DOA) and range information of the targets by utilizing symmetric nested frequency diverse array (SNFDA). Due to the frequency diverse array (FDA) owing time‐angle‐range‐dependent beampattern, the authors first properly design frequency increments and choose the sensor outputs to construct a fourth‐order cumulant matrix of SNFDA which is only related to DOAs of targets, and the authors estimate the DOAs of all targets by solving the ℓ 1 ‐norm minimization convex optimization problem. Then a mixed range‐dependent overcomplete dictionary with DOAs estimation is formulated in the compressive sensing framework to classify targets types by using periodic characteristics of the estimated range‐dependent beampattern. Finally, the range estimations of all targets are obtained via 1‐D range‐domain spectral searching. Compared with the existing mixed near‐field and far‐field targets localization methods, the proposed method jointly uses FDA angle‐range‐dependent beampattern and increased degrees‐of‐freedom of nested array, which can achieve both improved resolutions and accuracies in DOAs and ranges estimation for all targets. A set of numerical examples is reported and discussed to validate the superiority of the proposed algorithm.

The automatic identification of cardiac condition through dynamic modelling of electrocardiogram (ECG) signal has immense clinical significance as it eliminates the task of manual annotation of ECG recordings. In this study, an interacting multiple model (IMM)-based scheme has been proposed that helps in dynamically modelling and estimating the ECG signal. This model is having the adaptability of interchanging among several morphological representations. It offers the advantage of not necessitating user-specific parameters. This model does not necessitate a priori information about the ECG signal to initialise the filter parameters and delimitation of fiducial points of ECG signal. The particle filter (PF)-based schemes show superiority owing to their freedom from a single assumption on the signal model and noise model. Besides, it has got the potential of simultaneously tracking multiple pathological and morphological changes occurring in biomedical signals. Thus, the parameters of the model are estimated by adopting the PF, so that the myocardial infarction (MI) affected ECG signals can be efficiently tracked. Investigations on ECG signals from the MIT-BIH database and PTB diagnostic ECG database signify that the IMM-PF scheme can represent several MI morphologies with minimum prior information without distorting the helpful diagnostic information of ECG accurately.

In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

Speech signals degraded by noise tend to lose their quality and intelligibility. Therefore, the goal of speech enhancement algorithms is to restore these attributes of speech. The current work proposes a dynamic filter structure, dynamic over every utterance, that would vary simultaneously based on (i) the class of sound units in a given noisy/degraded signal, to improve intelligibility, and (ii) the noise components present, to improve quality. This filter structure is employed in the temporal-domain filtering-based multi-band speech enhancement algorithm, proposed by Jeeva et al.. The performance of the algorithm is evaluated subjectively and objectively, in terms of quality and intelligibility, and the algorithm is observed to successfully improve both attributes of degraded speech. Since the improvement in intelligibility depends on the effective restoration of sound units in the utterance, this process is language-specific. In this regard, an analysis is performed to study the influence of phone class distribution on the intelligibility improvement achieved for four Indian languages, namely Tamil, Hindi, Telugu, and Malayalam, and Indian English.

The traditional robust principal component analysis (RPCA) model is based on the nuclear norm, which usually underestimates the singular values of the low-rank matrix. As a consequence, the restoration image experiences serious interference by Gaussian noise, and the image quality degenerates during the denoising process. Therefore, an improved RPCA method via non-convex regularisation terms is proposed to remedy the above shortcomings. First, in order to estimate the singular value of the low-rank matrix more accurately, the authors employ the non-convex penalty function and add a weight vector to it. Then, the regularisation with non-convex penalty function and its weighted version are used to replace the nuclear norm and entry-wise norm in original RPCA, respectively, to establish an improved model. Finally, an optimal solution algorithm is derived by developing the alternating direction multiplier method. Experimental results show that the proposed method has better performance in terms of both quantitative measurement and visual perception quality than other several state-of-the-art image denoising methods.

Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper‐tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.