In this paper, we propose UILoc, an unsupervised indoor localization scheme that uses a combination of smartphone sensors, iBeacons and Wi-Fi fingerprints for reliable and accurate indoor localization with zero labor cost. Firstly, compared with the fingerprint-based method, the UILoc system can build a fingerprint database automatically without any site survey and the database will be applied in the fingerprint localization algorithm. Secondly, since the initial position is vital to the system, UILoc will provide the basic location estimation through the pedestrian dead reckoning (PDR) method. To provide accurate initial localization, this paper proposes an initial localization module, a weighted fusion algorithm combined with a k-nearest neighbors (KNN) algorithm and a least squares algorithm. In UILoc, we have also designed a reliable model to reduce the landmark correction error. Experimental results show that the UILoc can provide accurate positioning, the average localization error is about 1.1 m in the steady state, and the maximum error is 2.77 m.

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A kernel affine projection-like algorithm (KAPLA) is proposed in reproducing kernel Hilbert space in non-Gaussian environments. The cost function for the developed algorithm is constructed by using the correntropy approach and Gaussian kernel to deal with nonlinear channel estimation. The devised algorithm can efficiently operate in the impulse noise. As a consequence, the proposed KAPLA algorithm provides good performance for nonlinear channel equalization in implusenoise environments. Simulations results in different mixed noise environments verify the superior behavior of KAPLA compared to known algorithms.

The achievement of the boundary values of spectral efficiency is associated with the development of methods for generating and receiving signals with a compact spectrum. The reason is a constant increase in the capacity of existing communication channels caused by an increase in the volume of transmitted information. Moreover, the allocated frequency bandwidths have natural limitations, and they are almost all reached. The effective way to approach the boundary values of spectral efficiency is the application of spectrally efficient signals, such as FTN (Faster-Than-Nyquist) signals. This article proposes to synthesize optimal FTN signals that are more compact in the spectrum than RRC (root raised cosine) pulses-based signals. The criterion of maximum energy concentration in the occupied frequency bandwidth and the constraint on the cross-correlation coefficient are used to solve the optimization problem. The contribution of this work is the optimization of FTN signal shape. The obtained optimal FTN signals provide a 24% increase in spectral efficiency compared to the RRC pulses-based signals. At the same time, the energy loss stays almost unchanged. To the best of authors' knowledge, this is the first case when the frequency bandwidth reduction is achieved at practically no energy loss. The simulation modeling in channels with additive white Gaussian noise and fading channels is done with regard to the FTN-SC-FDE (Faster-Than-Nyquist-Single Carrier system with Frequency Domain Equalization) structure. The optimal FTN signals presented in this work can be used to increase the spectral efficiency of satellite broadcasting systems, such as DVB-S2/S2X. INDEX TERMS Faster than Nyquist signaling; maximum band energy concentration criterion; optimization methods; spectral efficiency

In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed.

An optimal electric dipole antennas model is presented and analyzed, based on the hemispherical grounding equivalent model and the superposition principle. The paper also presents a full-wave electromagnetic simulation for the electromagnetic field propagation in layered conducting medium, which is excited by the horizontal electric dipole antennas. Optimum frequency for field transmission in different depth is carried out and verified by the experimental results in comparison with previously reported simulation over a digital wireless Through-The-Earth communication system. The experimental results demonstrate that the dipole antenna grounding impedance and the output power can be efficiently reduced by using the optimal electric dipole antenna model and operating at the optimum frequency in a vertical transmission depth up to 300 m beneath the surface of the earth.

In this paper, a novel inversion method is proposed to recover the sharp boundary of blocky targets buried beneath the seabed with conductivities different from that of the background environment. This method is implemented by combining the Laplacian-of-Gaussian (LoG) function with minimum gradient support (MGS) regularization. A two-stage inversion strategy is introduced to obtain stable and sharp boundary inversion results. We first use the LoG operator in 3D space to obtain the profile of the target and then switch to LoG-MGS coupled regularized inversion to obtain the sharp boundary of the target. It is crucial to choose an appropriate regularization parameter adjustment strategy. We use a bounded function to adjust the regularization parameters during the inversion, which can balance the observation information and the a priori information in a reasonable interval. Theoretical analysis and numerical simulations are conducted and the recovered results demonstrate that the proposed inversion method has a better performance in recovering blocky targets than canonical regularization terms. INDEX TERMS Block targets, inversion, sharp boundary, LoG-MGS coupled regularization.