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5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • A new distributed Kalman filtering for information fusion is proposed. • K-means clustering technique is adapted to obtain the trusted nodes. • Information from the trusted nodes are used for fusion. • The proposed approach is robust to the SNR variation among the nodes. • It is robust to malicious cyber-attacks. • Compromised nodes detection and localization are byproducts. Abstract As one of the widely used applications in wireless sensor networks, target tracking has attracted considerable attention. Although many tracking techniques have been developed, it is still a challenging problem if the network is under cyber attacks. Inaccurate or false information is maliciously broadcast by the compromised nodes to their neighbors. They are likely to threaten the security of the system and result in performance deterioration. In this paper, a distributed Kalman filtering technique with trust-based dynamic combination strategy is developed to improve resilience against cyber attacks. Furthermore, it is efficient in terms of communication load, only local instantaneous estimates are exchanged with the neighboring nodes. Numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks. [ABSTRACT FROM AUTHOR]

The dominant multipath components for massive multiple-input multiple-output systems can be described using geometry-based channel models with R-dimensional (R-D) parameters. These parameters are crucial for channel correlation acquisition, which is a prerequisite for many technical challenges. In this paper, we consider higher-order singular value decomposition based R-D channel modeling parameter estimation from incomplete measurements. Incomplete higher-order orthogonality iteration algorithm can be utilized to solve the problem, which simultaneously achieves tensor recovery and tensor decomposition. After obtaining the signal or noise subspace, the parameters of interest can be estimated by using subspace methods. [ABSTRACT FROM AUTHOR]

Nonlinear system identification is an important and fundamental problem in many practical applications. It becomes more challenging when the noise is non-Gaussian. Inspired by the cognitive dynamic system concept, we propose a perception-based $$\ell _p$$ -norm minimization approach for nonlinear system identification in generalized Gaussian distribution noise environments. Volterra model is utilized to describe the nonlinear system. The proposed cognitive algorithm incorporates a closed feedback loop between perceptions and actions to the environments. Computer simulations have been carried out to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of this note is to point out that the recently proposed fractional least mean squares (FLMS) algorithm, whose derivation is based on fractional derivative, is not suitable for adaptive signal processing. Our claims are verified via extensive simulation results with comparison with the least mean squares (LMS) algorithm, indicating that the new method does not have any advantages over the classical one. [ABSTRACT FROM AUTHOR]

Robust received signal strength (RSS)-based cooperative wireless localization using a linear least squares (LLS) method is proposed. We first extend the LLS methods to the cooperative case. To make the proposed algorithm robust, dynamic path loss models are utilized to describe the changing environments; the path loss constants are updated based on the estimated positions of the client nodes. Furthermore, anchor nodes (reference nodes) calibration is considered to deal with the anchor node position errors. The simulation results are given for the proposed methods. Compared with the RSS-based non-cooperative LLS type methods, robust and better performance are obtained. [ABSTRACT FROM PUBLISHER]

Mobile data traffic is expected to have an exponential growth in the future. To meet the challenge of mobile data traffic exponential growth, massive multiple input, multiple output (MIMO) has been proposed as one of the enabling technologies for future wireless systems. Due to the huge number of antennas at the base station, it is more reasonable to rely on direction-of-arrival (DOA) estimation to perform downlink precoding. This is one of the main reasons why direction information becomes critical for such a system. The measurement of 2D massive MIMO can be expressed as a 3D tensor. In this letter, we propose an improved tensor method of direction estimation (IT-MODE) based DOA estimation algorithm. It is a modification of the conventional tensor method of direction estimation (T-MODE) algorithm, and the threshold performance is improved. Asymptotic efficiency is achieved for the proposed estimator even when the source signals are highly correlated or coherent. [ABSTRACT FROM PUBLISHER]

Higher-order singular value decomposition (HOSVD) is usually required in R-dimensional (R-D) harmonic retrieval, where R \geq 3. In this letter, we devise an iteratively reweighted HOSVD technique, which is referred to as IR-HOSVD, for multi-dimensional frequency estimation in the presence of impulsive noise. The main idea is to minimize the \ell _p-norm residual errors along all the R dimensions, where 1 < p < 2. After decomposition, standard subspace techniques can be applied for parameter estimation. Based on the numerical results, IR-HOSVD outperforms several state-of-the-art techniques in terms of root mean square frequency error for different impulsive noise models. [ABSTRACT FROM PUBLISHER]

We propose an indoor angle-of-arrival (AOA) estimation algorithm for IEEE 802.11ac Wi-Fi signal using single access point (AP) with multiple antennas. The auto-focusing method is first used to obtain a coherently combined matrix among different subcarrier frequencies. After that, we implement Toeplitz processing on the coherently combined matrix to decorrelate the received multipath signals in spatial domain. Simulation and experiment results in an indoor multipath environment show that our proposed method outperforms the conventional auto focusing method by a significant margin, when the multipath signals are highly correlated. For the case with low correlations, both methods can obtain the AOAs with high accuracy. The observations are validated by both simulations and indoor multipath experiments. [ABSTRACT FROM PUBLISHER]

We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on \15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over \81\% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject’s breathing. The feasibility of our proposed system is verified by both the simulation and experiment results. [ABSTRACT FROM PUBLISHER]

5G communication systems operating above 24 GHz have promising properties for user localization and environment mapping. Existing studies have either relied on simplified abstract models of the signal propagation and the measurements, or are based on direct positioning approaches, which directly map the received waveform to a position. In this study, we consider an intermediate approach, which consists of four phases—downlink data transmission, multi-dimensional channel estimation, channel parameter clustering, and simultaneous localization and mapping (SLAM) based on a novel likelihood function. This approach can decompose the problem into simpler steps, thus leading to lower complexity. At the same time, by considering an end-to-end processing chain, we are accounting for a wide variety of practical impairments. Simulation results demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Future intelligent transport systems depend on the accurate positioning of multiple targets in the road scene, including vehicles and all other moving or static elements. The existing self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in developing on-board perception systems stymie further improvements in the precision and integrity of positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming a standard component of intelligent and connected vehicles, renders new sources of information such as dynamically updated high-definition (HD) maps accessible. In this paper, we propose a unified theoretical framework for multiple-target positioning by fusing multi-source heterogeneous information from the on-board sensors and V2X technology of vehicles. Numerical and theoretical studies are conducted to evaluate the performance of the framework proposed. With a low-cost global navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-board sensors, and a normally equipped HD map, the precision of multiple-target positioning attained can meet the requirements of high-level automated vehicles. Meanwhile, the integrity of target sensing is significantly improved by the sharing of sensor information and exploitation of map data. Furthermore, our framework is more adaptable to traffic scenarios when compared with state-of-the-art techniques. [ABSTRACT FROM AUTHOR]