Non-line-of-sight Localization Research Articles

A Ray-Tracing-Based Single-Site Localization Method for Non-Line-of-Sight Environments.

Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, we propose a novel single-site localization method tailored for complex multipath NLOS environments, leveraging only angle-of-arrival (AOA) estimates in conjunction with a ray-tracing (RT) algorithm. The method transforms NLOS paths into equivalent line-of-sight (LOS) paths through the generation of generalized sources (GSs) via ray tracing. A novel weighting mechanism for GSs is introduced, which, when combined with an iteratively reweighted least squares (IRLS) estimator, significantly improves the localization accuracy of non-cooperative target sources. Furthermore, a multipath similarity displacement matrix (MSDM) is incorporated to enhance accuracy in regions with pronounced multipath fluctuations. Simulation results validate the efficacy of the proposed algorithm, achieving localization performance that approaches the Cramér-Rao lower bound (CRLB), even in challenging NLOS scenarios.

Non-Line-of-Sight Imaging and Vibrometry Using a Comb-Calibrated Coherent Sensor.

Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects, allowing a wide range of applications. Existing NLOS systems rely on pulsed lasers and time-resolved single-photon detectors to capture the information encoded in the time of flight of scattered photons. Despite remarkable advances, the pulsed time-of-flight LIDAR approach has limited temporal resolution and struggles to detect the frequency-associated information directly. Here, we propose and demonstrate the coherent scheme-frequency-modulated continuous wave calibrated by optical frequency comb-for high-resolution NLOS imaging, velocimetry, and vibrometry. Our comb-calibrated coherent sensor presents a system temporal resolution at subpicosecond and its superior signal-to-noise ratio permits NLOS imaging of complex scenes under strong ambient light. We show the capability of NLOS localization and 3D imaging at submillimeter scale and demonstrate NLOS vibrometry sensing at an accuracy of dozen Hertz. Our approach unlocks the coherent LIDAR techniques for widespread use in imaging science and optical sensing.

Towards passive non-line-of-sight acoustic localization around corners using uncontrolled random noise sources

Non-line-of-sight (NLoS) imaging is an important challenge in many fields ranging from autonomous vehicles and smart cities to defense applications. Several recent works in optics and acoustics tackle the challenge of imaging targets hidden from view (e.g. placed around a corner) by measuring time-of-flight information using active SONAR/LiDAR techniques, effectively mapping the Green functions (impulse responses) from several controlled sources to an array of detectors. Here, leveraging passive correlations-based imaging techniques (also termed ’acoustic daylight imaging’), we study the possibility of acoustic NLoS target localization around a corner without the use of controlled active sources. We demonstrate localization and tracking of a human subject hidden around a corner in a reverberating room using Green functions retrieved from correlations of broadband uncontrolled noise sources recorded by multiple detectors. Our results demonstrate that for NLoS localization controlled active sources can be replaced by passive detectors as long as a sufficiently broadband noise is present in the scene.

Computational framework for steady-state NLOS localization under changing ambient illumination conditions.

Non-line-of-sight (NLOS) imaging of hidden objects is a challenging yet vital task, facilitating important applications such as rescue operations, medical imaging, and autonomous driving. In this paper, we attempt to develop a computational steady-state NLOS localization framework that works accurately and robustly under various illumination conditions. For this purpose, we build a physical NLOS image acquisition hardware system and a corresponding virtual setup to obtain real-captured and simulated steady-state NLOS images under different ambient illuminations. Then, we utilize the captured NLOS images to train/fine-tune a multi-task convolutional neural network (CNN) architecture to perform simultaneous background illumination correction and NLOS object localization. Evaluation results on both stimulated and real-captured NLOS images demonstrate that the proposed method can effectively suppress severe disturbance caused by the variation of ambient light, significantly improving the accuracy and stability of steady-state NLOS localization using consumer-grade RGB cameras. The proposed method potentially paves the way to develop practical steady-state NLOS imaging solutions for around-the-clock and all-weather operations.

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

SummaryVehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches.

Three Passive TDOA-AOA Receivers Based Flying-UAV Positioning in Extreme Environments

In this paper, motivated by the fact that precisely positioning a flying unmanned aerial vehicle (UAV) by passive radio frequency (RF) measurements are being troubled by the extreme non-line-of-sight (NLOS) environments, we propose a 3D joint time difference of arrival (TDOA) and angle of arrival (AOA) NLOS localization method for flying UAVs using three receivers. Compared with the existing positioning schemes based on the received signal strength (RSS), TDOA, time of arrival (TOA), and AOA methods, the advantage of the proposed method is that we do not need many receivers or priori NLOS statistics, which significantly decreases the cost in practice. Simulation results demonstrate that the positioning mean square error (MSE) of the proposed method is closer to the Cramer-Rao lower bound of TDOA-based method than the existing positioning methods. Moreover, the simulation results verified the reliability of the proposed method.

A TOA-Based Localization Algorithm With Simultaneous NLOS Mitigation and Synchronization Error Elimination

Time of arrival (TOA) is one of the most commonly used localization techniques in wireless networks. However, it requires strict time synchronization between source and base stations or anchor nodes, and nonline-of-sight (NLOS) propagation significantly degrades the localization accuracy. In this article, the condition of perfect time synchronization is relaxed, and synchronization error is introduced into TOA-based NLOS localization model, which can be suitable for a asynchronous wireless network. A simultaneous NLOS mitigation and synchronization error elimination localization algorithm consisting of two steps is proposed. In the first step, by defining two new variables, we transform nonlinear range measurements into linear forms and construct a quadratic programming model. In the second step, by analyzing the error vector and the nonlinear relation among variables, a linear model is derived. Simulation results show that the proposed algorithm outperforms the existing algorithms in different NLOS conditions. Average location error of our proposed algorithm is below 100 m when the maximum NLOS error is 100 m and synchronization error is 1 μs. Moreover, compared with the existing algorithms, the localization accuracy of our proposed algorithm is improved by more than 50% on average. Further, the computational complexities of different algorithms are analyzed and compared.

Weighted inertia-based dynamic virtual bat algorithm to detect NLOS nodes for reliable data dissemination in VANETs

Vehicular ad hoc network (VANET) is the significant network suitable for the deployment of risk-free environment that ensures least congestion and secure collaboration among the vehicular nodes of the network. The maintenance of connectivity among vehicular nodes is influenced by non line of sight (NLOS) nodes by introducing broadcasting storm and channel congestion during data dissemination. The problem of the NLOS node localization is considered as the optimization process for reducing the latency incurred in emergency information distribution. In this paper, a weighted inertia-based dynamic virtual bat algorithm (WIDVBA) is proposed for enhancing the characteristics of the traditional Virtual bat scheme that integrates the merits of simulated annealing (SA) and particle swarm optimization (PSO) for effective NLOS node localization. This proposed WIDVBA prevents the problem of premature convergence by incorporating the benefit of a weighted inertial factor compared to the traditional dynamic virtual binary bat-based NLOS localization approaches. This proposed WIDVBA dynamically increase and decrease the area of exploration and degree of exploration depending on the location of the NLOS nodes. The performance of WIDVBA studied using EstiNet 8.1 reveals that the number of NLOS nodes and time incurred in detecting NLOS nodes potentially increases with an increase in the neighbor awareness rate on par with the compared NLOS detection techniques.

A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks

The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

Elliptical Lagrange-Based NLOS Tracking Localization Scheme

This paper presents a novel tracking localization scheme that requires only a single dominant nonline-of-sight (NLOS) path and its corresponding scattering point. The position of the scattering point arising out of the dominant one bounce NLOS scattering path can be estimated using the proposed two-step elliptical Lagrange constrained optimization method if the initial position of the mobile device (MD) can be estimated or when the MD transits from line-of-sight (LOS) to a NLOS region. With the dominant NLOS path and corresponding scattering point, the subsequent trajectory of the MD can be estimated. Simulation and empirical data through channel measurement experimentation in two different multipath environments have shown that our proposed NLOS localization scheme surpassed existing NLOS localization schemes by significant margins, especially at high TOA and AOA noise due to, e.g., low bandwidth and low signal-to-noise ratio (SNR).

RSS-based indoor localization with PDR location tracking for wireless sensor networks

Traditional indoor localization techniques typically rely on a single communication source such as RF signals, ultrasound, inertial sensors, and geomagnetic fields. In wireless sensor network (WSN), received signal strength (RSS) based approach is one of the most commonly used localization techniques. It is simple to implement and costly efficient, but the estimation accuracy is significantly reduced in non-line-of-sight (NLOS) conditions. pedestrian dead reckoning (PDR) uses inertial measurement unit (IMU) for location tracking. Its tracking accuracy is not affected by the interference in NLOS conditions, but the location error is accumulated in long distance tracking. We propose the RSS-based indoor localization algorithm combined with PDR location tracking for wireless sensor networks. The objective is to compensate the NLOS localization error by using PDR, while mitigating the accumulated error of PDR by using RSS-based localization method in LOS conditions. Lab scale experiment and large scale simulation results show that the proposed algorithm significantly reduce the location estimation errors in wireless sensor networks.

An Indoor Mobile Location Estimator in Mixed Line of Sight/Non-Line of Sight Environments Using Replacement Modified Hidden Markov Models and an Interacting Multiple Model.

Localization as a technique to solve the complex and challenging problems besetting line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions has recently attracted considerable attention in the wireless sensor network field. This paper proposes a strategy for eliminating NLOS localization errors during calculation of the location of mobile terminals (MTs) in unfamiliar indoor environments. In order to improve the hidden Markov model (HMM), we propose two modified algorithms, namely, modified HMM (M-HMM) and replacement modified HMM (RM-HMM). Further, a hybrid localization algorithm that combines HMM with an interacting multiple model (IMM) is proposed to represent the velocity of mobile nodes. This velocity model is divided into a high-speed and a low-speed model, which means the nodes move at different speeds following the same mobility pattern. Each moving node continually switches its state based on its probability. Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms. Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM. The simulations also show that the three algorithms are accurate, stable, and robust.

Omnidirectional Mobile NLOS Identification and Localization via Multiple Cooperative Nodes

This paper presents an Omnidirectional mobile target-node (TN) localization technique in nonline-of-sight (NLOS) scenarios. Here, a TN cooperates with base-nodes (BNs) with antenna-arrays to allow them to find its position via time-of-arrival (TOA) and direction-of-arrival (DOA) measurements. When line-of-sight (LOS) channel is available, each BN localizes TNs in its coverage area with reasonable accuracy. However, when LOS channel is obstructed, considerable localization error is generated. To avoid NLOS error, a technique is proposed to identify if a TN is in the LOS of multiple BNs or not. The technique enables BNs to determine and localize their shared reflection points, and to localize NLOS TNs. The paper assumes single-bounce-reflection NLOS channel between BNs and TNs. In NLOS scenarios, when three or more reflection points are shared by a TN and multiple sets of BNs, the shared reflection points are localized via DOA fusion, and then the TN is localized via TOA fusion. The equations for NLOS identification, shared reflection points determination and localization and NLOS TN localization are theoretically derived. Simulations are conducted to evaluate the proposed technique in terms of the probability of false alarm and misdetection of NLOS identification and shared reflection points determination, and NLOS TN localization accuracy.

Localization in mobile wireless and sensor networks

Accurate localization or tracking of wireless devices is a crucial requirement for many emerging location-aware systems. Fields of applications include search and rescue, medical care, intelligent transportation, locationbased billing, security, home automation, industrial monitoring and control, location-assisted gaming, and social networking. During the last few years, there have been intensive research activities in this area and various solutions have been investigated. The main trend now is toward the integration of heterogeneous technologies to ensure global coverage and high accuracy in all possible scenarios, leading to a seamless localization system available anywhere anytime. While satellite-based navigation is well consolidated for open sky scenarios, localization in harsh environments (e.g., indoor or in urban canyons) is still an open issue that requires complementary wireless networks. Cellular systems, local/personal area networks, ad hoc, and wireless-sensor networks can be configured to support localization functionality. Indoor environments, however, are particularly challenging because of severe multipath and non-line-of-sight (NLOS) propagation. In this context, advanced signal processing algorithms must be employed in order to guarantee positioning robustness, such as NLOS identification and mitigation, fusion of data from different sources, and Bayesian methods to enclose any a priori information (e.g., dynamic models for mobile positioning). An important area of research is cooperative localization, which is expected to significantly improve both accuracy and coverage by exploiting all the available measurements on a peer-to-peer basis; efficient protocols and procedures have to be designed to minimize communication overheads and energy consumption. Measurement campaigns are essential for calibrating signal models and testing localization algorithms. A valuable tool for benchmarking algorithms is also provided by fundamental performance bounds, which are being actively analyzed as guidelines for the design of efficient positioning systems. The objective of this special issue, which was promoted under the auspices of the EC Network of Excellence in Wireless Communications NEWCOM++ (in particular, the Work Package WPR.B on Localization and Positioning Techniques), was to gather recent advances in both signal processing and communications areas, for localization in mobile wireless and sensor networks. Articles were solicited on both experimental and theoretical aspects, including new positioning algorithms and methodologies, system design and configuration, performance analysis and measurement campaigns. We received a total of 56 manuscripts addressing the above issues and challenges, of which 16 were selected for publication. Selection of each article was the result of a careful assessment by at least two (mostly three) independent reviewers with expertise on localization and wireless networking. Articles went through a minimum of two to a maximum of four revision phases before acceptance. Accepted articles belong to four main research areas: integration of positioning and communication functionalities, robustness to NLOS errors, indoor positioning, and localization in wireless sensor networks (WSNs). The first group of articles deals with the interaction of positioning and communications at different layers of the protocol stack. Connectivity issues are studied in Gao et al., which considers the relation between distance and communication hops, accounting for the border effect and dependence problems, for a model that is more realistic than the traditional unit-disk graph model. A related problem is investigated in the study of Moragrega et al., which deals with location-aware cluster formation. The authors propose LACFA, a distributed network formation algorithm that significantly increases the probability of localization of sensors in a cluster-tree topology. On the physical layer, Schmeink et * Correspondence: nicoli@elet.polimi.it Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy Full list of author information is available at the end of the article Nicoli et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:197 http://jwcn.eurasipjournals.com/content/2011/1/197