Multipath Propagation Environment Research Articles

Single-base station hybrid positioning algorithm based on LOS identification

In the complex multipath propagation environment, whether there is a line of sight (LOS) path will directly affect the positioning accuracy. Therefore, this paper proposes a single base station hybrid positioning algorithm based on LOS identification. In the algorithm, we first construct multiple features based on channel state information with LOS and without LOS in statistical distribution and use these features and gradient boosting decision tree to determine whether there is a LOS path in the environment. Then for the environment with a LOS path, we proposed a positioning method based on the estimation of signal parameters via rotation invariant technology algorithm which can be used to jointly estimate the angle of arrival and time delay of the LOS path for positioning, while for the environment without LOS path, we propose a positioning method based on adaptive genetic algorithm. Finally, a single base station hybrid positioning algorithm based on LOS identification and the corresponding positioning methods. Simulation results show that the proposed hybrid positioning algorithm can achieve high-precision positioning in the complex multipath propagation environment.

A method for resolving the phase difference ambiguity in interferometric monopulse angle of arrival estimation

This paper presents a method to eliminate the phase difference ambiguity in angle of arrival (AOA) estimation using the radio-frequency-based interferometric monopulse technique. In order to uniquely determine the AOA, we utilize four directivities which are formed by using selected elements with the same weight function as the AOA estimation. We investigate the AOA estimation accuracy for the direction of the incoming wave from a high elevation angle and in multi-path propagation environments using a three-dimensional fading emulator. The results show that the proposed method is valid for complex propagation environments.

Multi-Domain Cooperative SLAM: The Enabler for Integrated Sensing and Communications

Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple antennas, and a large number of connections present opportunities for deep integration of sensing and communications. First, the development of integrated sensing and communications (ISAC) is reviewed in this study, and the differences between ISAC and traditional communication are revealed. Then, efficient mechanisms for multi-domain collaborative SLAM are presented. In particular, research opportunities and challenges for cross-sensing, cross-user, cross-frequency, and cross-device SLAM mechanisms are proposed. In addition, SLAM-aided communication strategies are explicitly discussed. We prove that the multi-domain cooperative SLAM mechanisms based on hybrid sensing and crowdsourcing can considerably improve the accuracy of localization and mapping in complex multipath propagation environments through numerical analysis. Furthermore, we conduct testbed experiments to show that the proposed SLAM mechanisms can achieve decimeter-level localization and mapping accuracy in practical scenarios, thereby proving the application prospect of multi-domain collaborative SLAM in ISAC.

An Iterative Implementation-Based Approach for Joint Source Localization and Association Under Multipath Propagation Environments

We address the problem of joint source localization and association (JSLA) under a multipath propagation environment for sensor arrays. Taking into account inaccurate prior information in practical applications, we propose a JSLA algorithm based on the iterative implementation of the minimum mean-square error (MMSE) framework with semi-unitary and sparsity constraints and the subspace technique. The proposed algorithm exploits benefits of both spatial signals' sparse characteristic and coherence structure when localizing unknown sources in a mixed interference environment. In contrast with previous works, the proposed algorithm can associate the incident paths to each source from the complex propagation environment with improved association performance even under low signal-to-noise ratio conditions. Neither additional decorrelation preprocessing nor prior information pertaining to the multipath propagation is required. Both simulations and real data experiments demonstrate the effectiveness and robustness of the proposed algorithm.

High-Accuracy Localization in Multipath Environments via Spatio-Temporal Feature Tensorization

High-accuracy position awareness is essential to many applications, such as indoor navigation and autonomous vehicles. Wireless localization is a promising positioning service provider with the merits of extensive coverage and low cost but degrades in complex multipath propagation environments. This paper proposes a tensor-based algorithmic framework for localization in multipath environments by exploiting sparse spatio-temporal features of the received waveforms. Specifically, we construct low-rank tensors to characterize sparse spatio-temporal features, which can separate coherent multipath signals hinging on the uniqueness of tensor decomposition. Compared with related tensor-based works, our method does not rely on array configurations or signal structures, revealing its potential for broad use in multipath estimation. Position-related parameters are further extracted from tensor decomposition results, where a method based on the Chinese remainder theorem (CRT) is developed to retrieve distance information from the carrier part. Simulation results show that our method yields high-accuracy localization performance in complex multipath environments.

Security Assessment of Phase-Based Ranging Systems in a Multipath Environment

Phase-based ranging has been widely deployed in proximity detection scenarios including security-critical applications due to their low implementation complexity on existing transceivers. In this work, the security of multi-carrier phase-based ranging systems in a multipath propagation environment is investigated. We present a threat model that can successfully target any decreasing distance in different multipath environmental conditions rendering the phase-based ranging method insecure. We assess the feasibility of attacks in various attack scenarios through simulations using a multipath channel and demonstrate a simplified version of the attacker model implemented in hardware. We show that the attacker can spoof the measured distance to less than one meter when the devices are separated by 30 meters. The evaluation of possible countermeasures and their limitations for different threat models is performed.

An efficient RBF‐DCNN based DOA estimation in multipath and impulse noise wireless environment

AbstractThe typical MultiPath (MP) propagation environment attains lots of MP signals as of the antenna array, and the Impulse Noise (IPN) accompanies these MP signals. The Directions‐of‐Arrivals (DOA) of an MP signal is tough to be assessed because of these IPN. Though numerous existing techniques were generated to assess the MP signal's DOA, they cannot attain a desirable output. To achieve robust and accurate DOA estimation (DOAE) in multipath and IPN wireless environment, this work proposes an efficient Radial Basis Function based Deep Convolutional Neural Network (RBF‐DCNN) centered DOAE. Here, initially, the noises in the inputted signal are eradicated utilizing Improved Particle Filter (IPF). After denoising, the imperative features are extracted as of that signal. Next, the required features are selected as of that signal, which is utilized for Dimension Reduction (DR). The Modified Salp Swarm Optimization Algorithm (MSSOA) is employed for feature selection. Then, the dimension reduced signal is inputted to the RBF‐DCNN, which estimated the DOA centered upon the received signal's SNR value. Numerical simulation outcomes are rendered to exemplify the proposed method's effectiveness.

Target Height Measurement under Complex Multipath Interferences without Exact Knowledge on the Propagation Environment

This paper investigates the direction-of-arrival (DOA) estimation-based target localization problem using an array radar under complex multipath propagation scenarios. Prevalent methods may suffer from performance degradation due to the deterministic signal model mismatch, especially when the exact knowledge of a propagation environment is unavailable. To cope with this problem, we first establish an improved signal model of multipath propagation for low-angle target localization scenarios, where the dynamic nature of convoluted interferences induced by complex terrain reflections is taken into account. Subsequently, an iterative implementation-based target localization algorithm with the improved propagation model is proposed to eliminate the detrimental effect of coherent interferences on target localization performance. Compared to existing works, the proposed algorithm can maintain satisfactory estimation performance in terms of target location parameters, even in severe multipath interference conditions, where the decorrelation preprocessing and accurate knowledge about the multipath propagation environment are not required. Both simulation and experimental results demonstrate the effectiveness of the proposed propagation model and localization algorithm.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment. The highly deterministic maximum likelihood estimator has a high accuracy, but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method. In this paper, a robust estimation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets. The compound reflection coefficient is estimated from the received data of the array and then a one-dimension generalized steering vector is constructed to estimate the target height. The algorithm is robust to the reflecting surface height error and the ground reflection coefficient error. Finally, the experiment and simulation results demonstrate the validity of the proposed method.

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The main idea is that the dissimilarity between the channel impulse responses (CIRs) increases with d and allows for an estimate <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\hat{d}$</tex></formula> . Our approach relies on extracting common multipath components (MPCs) from the CIRs. This is realistic in indoor or urban scenarios and if d is considerably smaller than the distances to the observers. We present distance estimators which utilize the rich location information contained in MPC delay differences. Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without time synchronization, delay measurement errors, and knowledge of the MPC association between the two CIRs. The estimators exhibit great technological advantages: they do not require line-of-sight conditions or knowledge about the observer locations or the environment. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. We describe how the paradigm could be incorporated into established cooperative localization frameworks to provide them with novel inter- and intra-node measurements for spatio-temporal tracking. This could enable low-cost localization of wireless users or networks in dynamic multipath settings.

Iterative Implementation Method for Robust Target Localization in a Mixed Interference Environment

For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semiunitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical Earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Radio Channel Capacity with Directivity Control of Antenna Beams in Multipath Propagation Environment.

The basic technology that will determine the expansion of the technical capabilities of fifth generation cellular systems is a massive multiple-input-multiple-output. Therefore, assessing the influence of the antenna beam orientations on the radio channel capacity is very significant. In this case, the effects of mismatching the antenna beam directions are crucial. In this paper, the methodology for evaluating changes in the received signal power level due to beam misalignment for the transmitting and receiving antenna systems is presented. The quantitative assessment of this issue is presented based on simulation studies carried out for an exemplary propagation scenario. For non-line-of-sight (NLOS) conditions, it is shown that the optimal selection of the transmitting and receiving beam directions may ensure an increase in the level of the received signal by several decibels in relation to the coaxial position of the beams. The developed methodology makes it possible to analyze changes in the radio channel capacity versus the signal-to-noise ratio and distance between the transmitter and receiver at optimal and coaxial orientations of antenna beams for various propagation scenarios, considering NLOS conditions. In the paper, the influence of the directional antenna use and their direction choices on the channel capacity versus SNR and the distance between the transmitter and receiver is shown.

Distributed MIMO sonar for detection of moving targets in shallow sea environments

A joint processing framework is proposed for distributed multiple-input-multiple-output (MIMO) sonar systems to exploit spatial diversity and array gain for detection of moving targets. An extended moving target consisting of multiple scatterers is illuminated by widely-spaced transmitters from different angles to achieve spatial diversity. Target bearing is first estimated with high resolution by the deconvolved conventional beamforming technique. Then a replica correlation integration processor is utilized to estimate the target’s range and speed in a multipath propagation environment. A MIMO detector is designed with the estimated target position parameters and Doppler factor under the generalized likelihood ratio test framework. The effectiveness of the distributed MIMO sonar system has been validated by the localization of two moving targets in sea experiments.

Enabling Robust Radar-Based Localization and Vital Signs Monitoring in Multipath Propagation Environments.

Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation. We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization. We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting. The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections. Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes.

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Wireless Communications Considering Doppler Effect

In wireless systems aided by reconfigurable intelligent surfaces (RISs), channel state information plays a pivotal role in achieving the performance gain of RISs. Mobility renders accurate channel estimation (CE) more challenging due to the Doppler effect. In this letter, we propose two practical wideband CE schemes incorporating Doppler shift adjustment (DSA) for multi-path and single-path propagation environments, respectively, for RIS-assisted communication with passive reflecting elements. For the multi-path scenario, ordinary CE is first executed assuming quasi-static channels, followed by DSA realized via joint RIS reflection pattern selection and transformations between frequency and time domains. The proposed CE necessitates only one more symbol incurring negligible extra overhead compared with the number of symbols required for the original CE. For the single-path case which is especially applicable to millimeter-wave and terahertz systems, a novel low-complexity CE method is devised capitalizing on the form of the array factors at the RIS. Simulation results demonstrate that the proposed algorithms yield high CE accuracy and achievable rate with low complexity, and outperform representative benchmark schemes.

An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network

DoA estimation using channel state information (CSI) is an important technology for localization in Wi-Fi network. Existing direction of arrival (DoA) estimation algorithms such as multiple signal classification (MUSIC) algorithm may provide feasible DoA estimation accuracy, if the array gain-phase error can be well calibrated in commodity Wi-Fi devices. However, for multipath propagation environment, classical subspace-based calibration methods cannot be directly deployed, due to the coupling effect between gain-phase error parameters and DoA parameters. Therefore, most works deploy wired calibration methods with specialized devices, which also require professional and trivial deployments. This paper offers an iterative pairwise antenna gain-phase error calibration method, which is easy deployment and low cost. Furthermore, we combine spatial smoothing technique with pairwise antenna calibration schemes to guarantee calibration accuracy and parameters decoupling, respectively. Experimental results in realistic indoor environments to verify the effectiveness of the proposed calibration method.

Assessment of Uplink Massive MIMO in Scattering Environment

This paper investigates the performance of massive MIMO systems under the effect of multipath propagation environment. Linear Minimum Mean Squared Error (MMSE) is considered to assess the performance of BPBK/OFDM based uplink massive MIMO transmission. Bit Error Rate (BER) and channel capacity in Non Line Of Site (NLOS) multipath fading environment are presented. The results show a correlation between the number of antennas and the performance of the system.

A Benchmark for Antenna Correlation Measurements

Multielement antennas have become a central part of most communication systems, and yet their design and evaluation remain a challenge. A key parameter is the correlation matrix, which relates the antenna patterns and the multipath propagation. The correlation governs communication metrics such as the various capacities, error rates, and outages. Calculation of the correlation matrix requires accurate measurements of the complex far-field patterns of the antennas, and there is a need to verify that both the measurement and the calculation are accurate. An approach is presented for benchmarking the measured correlation accuracy by using a biconical antenna with a closed-form expression for its wideband correlation function. The approach is demonstrated by checking both physical and numerical experiments (simulation) against the theory. It benchmarks the accuracy of both the measurement and calculation stages for the antenna correlation in a rich isotropic multipath propagation environment.

LRT Detectors for Spectrum Sensing of Weak OFDM Signals With Periodic Pilots

Orthogonal frequency division multiplexing (OFDM) signals are widely employed in most wireless comm- unication systems, and the problem of spectrum sensing for OFDM signals has been extensively investigated. However, most of the available research reports on spectrum sensing for OFDM signals ignore the existence of pilots, which are usually inserted in the OFDM signals for synchronization, channel estimation and control information delivery. To sense weak OFDM signals with periodic pilots reliably in cognitive radio networks, we propose two likelihood ratio test (LRT) detectors for the scenarios with and without time synchronization information. In the ideal scenario that an unlicensed secondary user is perfectly synchronized with a licensed primary user, we propose a near-optimal LRT (NOLRT) detector, which constructs the test statistic by correlating the locally generated pilots with the received OFDM signal samples. Since the NOLRT detector relies on perfect time synchronization that is difficult to realize in practice, we further propose a more practical buffer-aided LRT (BALRT) detector that does not require time synchronization. To formulate the test statistic, the BALRT detector stores the averages of historical OFDM signal samples in a buffer and then correlates them with the currently received OFDM signal samples. Theoretical closed-form expressions for the probability of detection and probability of false alarm are derived for the proposed detectors. To alleviate the negative effects of noise uncertainty in low SNR scenarios, we also propose an effective method based on noise power estimation. Monte Carlo simulation results match with theoretical results very well and show that the NOLRT detector outperforms the counterparts significantly in additive white Gaussian noise (AWGN) channel with perfect time synchronization, while the BALRT detector outperforms the counterparts noticeably in multipath propagation environments without time synchronization.

Target Localization in High-Coherence Multipath Environment Based on Low-Rank Decomposition and Sparse Representation

In a multipath propagation environment, prevalent target localization methods are mainly based on the classical two-ray propagation model without considering other reflected waves. Because the received target echoes are considerably corrupted by multipath reflections in the case of complex terrain, these prevalent methods might fail to work or achieve poor performance. To solve this problem, we first consider a practical multipath propagation scenario to reveal the dynamic structural relationship of the spatial paths based on the spherical earth model. Subsequently, a target localization algorithm based on low-rank decomposition (LRD) and sparse representation (SR) framework is proposed. The proposed algorithm can effectively mitigate the effects of complex multipath interference without using any prior knowledge on the illuminated terrain and the reflecting paths. Experiments on synthetic data and real data validate the effectiveness of the proposed algorithm.