Non-line-of-sight Research Articles

Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.

A novel sparse-based approach for joint radio frequency fingerprint and channel estimation

Radio Frequency Fingerprint (RFF) plays a pivotal role in specific emitter identification (SEI) and enhancing the physical layer security of wireless networks. However, past research on RFF extraction has encountered significant challenges, including algorithm instability in the presence of multi-path channels, disparities between actual RFF and proposed models, and the need to compensate for receiver RFF. In this study, we introduce an innovative algorithm designed to simultaneously estimate the transmitter's RFF and the non-line-of-sight (NLOS) channel state in the presence of receivers’ antenna effect. Moreover, we introduce the transmitter antenna feeding current as a modified RFF, accounting for transimtter components such as power amplifier (PA) nonlinearity, in-phase quadrature (IQ) modulator imbalance. Our approach commences with an exploration of wave propagation theory fundamentals, followed by a transformation of the inverse scattering equations into linear equations for joint channel and RFF estimation. Due to the non-convex nature of the derived equation, we employ the sparse lift method to render the problem convex. Leveraging the sparsity of channel coefficients, we devise a compressive sensing linear solution, denoted as the sparse-based approach for joint (SBJ) RFF and channel estimator. Additionally, we propose a calibration method to mitigate the effect of the receiver antennas on the estimated RFF of the transmitter. Through simulation, we demonstrate a 10 dB enhancement in RFF estimation accuracy compared to existing methods. Furthermore, our results indicate that the SBJ algorithm remains robust when faced with the combined effect of transmitter components, whereas previous models exhibit diminished precision as additional component effects are considered.

An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

Fast and Fault-Tolerant Passive Hyperbolic Localization Using Sensor Consensus

The accuracy of passive hyperbolic localization applications using Time Difference of Arrival (TDOA) measurements can be severely compromised in non-line-of-sight (NLOS) situations. Consensus functions have been successfully used to provide robust and accurate location estimates in such challenging situations. In this paper, a fast branch-and-bound computational method for finding the global maximum of consensus functions is proposed and the global convergence property of the algorithm is mathematically proven. The performance of the method is illustrated by simulation experiments and real measurements.

Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.

Investigation of Characteristics of Ultraviolet Light Pulse Weak Signal Communication System Based on Fourth-Order Frequency-Shift Keying Modulation

In ultraviolet (UV) communication, On–Off Keying (OOK) is the primary modulation technique. Compared to OOK, frequency modulation offers stronger resistance to path attenuation. Currently, research on frequency modulation demodulation schemes for UV communication is limited, mainly employing waveform detection and laser pulse response methods, which require high detection sensitivity to light. This study presents a novel frequency modulation communication scheme using discrete Poisson channel distribution and optical pulse signal processing algorithms, enhancing the signal processing sensitivity of the existing frequency modulation scheme to the level of photons. The proposed system model is rigorously evaluated through theoretical derivations and simulations. Additionally, a hardware system integrating optical pulse counting, frequency detection, and clock data recovery algorithms is developed. Experimental results show the system achieving a 5 kbps transmission rate under frequency modulation. In non-line-of-sight (NLOS) scenarios, communication reaches up to 65 m, with the receiver elevation angle ranging from 10° to 25° and the bit error rate (BER) stabilized at 10−4, while in line-of-sight (LOS) situations, the BER remains at 10−5 up to 400 m and 10−4 up to 700 m, achieving the farthest distance and fastest communication rate achievable in the current FSK modulation scheme of ultraviolet communication systems. The integrated components enhance its applicability in communication systems. This study offers a valuable addition to UV communication technology.

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

A Stochastic Model Based on Optimal Satellite Subset Selection Strategy for Smartphone Pseudorange Relative Positioning.

In order to overcome the limitations of traditional stochastic models for smartphones, we introduce a double-difference code pseudorange residual (DDPR)-dependent stochastic model based on an optimal satellite subset, with the goal of mitigating the constraints imposed by the quality of GNSS observations in smartphones on the accuracy and reliability of phone-based GNSS positioning. In our methodology, the satellite selection process involved considering the integrated carrier-to-noise density ratio (C/N0) index of both the reference station and the smartphone, enabling us to construct a satellite subset characterized by superior observation quality. Furthermore, by leveraging the optimal subset of satellites and incorporating the C/N0-dependent stochastic model, we could determine the approximate location of the terminal through pseudorange differential positioning. Subsequently, we estimated the DDPRs for all satellites and utilized these values as prior information to build a stochastic model of the observations. Our findings indicate that in occluded environments, the DDPR-dependent stochastic model significantly enhances positioning accuracy for both the Huawei Mate40 and P40 terminals compared to the C/N0-dependent model. Numerically, the improvements in the north (N), east (E), and up (U) directions were approximately 30%, 32%, and 34% for the Mate40, and 26%, 33%, and 24% for the P40 terminal. This suggests that the proposed DDPR-dependent stochastic model effectively identifies and mitigates large gross error signals caused by multipath and non-line-of-sight (NLOS) signals, thereby assigning lower weights to these problematic observations and ultimately enhancing positioning accuracy. Moreover, the weighting method involves minimal computations and is straightforward to implement, making it particularly suitable for GNSS positioning applications on smartphones in complex urban environments.

A Hopular based weighting scheme for improving kinematic GNSS positioning in deep urban canyon

Global navigation satellite system (GNSS) positioning performance in the urban dense environment experiences significant deterioration due to frequent non-line-of-sight (NLOS) and multipath errors. An accurate weighting scheme is critical for positioning, especially in urban environment. Traditional methods for determining the weights of observations typically rely on the carrier-to-noise density ratio (C/N0) and the elevations from satellites to receivers. Nevertheless, the performance of these methods is degraded in the dense urban settings, as C/N0 and elevation measurements fail to fully capture the intricacies of NLOS and multipath errors. In this paper, a novel GNSS observations weighting scheme based on Hopular GNSS signal classifier, which can accurately identify the LOS/NLOS signals using medium-sized training dataset, is proposed to improve the urban kinematic navigation solution in real-time kinematic positioning mode. Four GNSS features: C/N0, time-differenced code-minus-carrier, loss of lock indicator and satellite’s elevation, are employed in the training of the Hopular based signal classifier. The performance of the new method is validated using two urban kinematic datasets collected by a U-blox F9P receiver with a low-cost antenna, in downtown Calgary. For the first testing dataset, the results show that the Hopular based weighting scheme outperforms the three most commonly used GNSS observations weighting schemes: C/N0, elevation, and a combined C/N0-elevation approach. Approximately 10.089 m of horizontal root-mean-squared (RMS) positioning error and 12.592 m of vertical RMS error are achieved using the proposed method; with improvements of 78.83%, 46.82% and 43.27% on horizontal positioning accuracy and 54.00%, 47.51% and 49.69% on vertical positioning accuracy, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively. For the second testing dataset, a similar performance is achieved with nearly 11.631 m of horizontal RMS error and 10.158 m of vertical RMS error; improvements of 64.58%, 32.90% and 22.40% on horizontal positioning accuracy and 71.99%, 65.24% and 55.88% on vertical positioning accuracy are achieved, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively.

Exploiting Anchor Links for NLOS Combating in UWB Localization

UWB (Ultra-wideband) has been shown as a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this paper, we propose RefLoc that leverages the unique benefits of UWB to address the NLOS problem. While we find NLOS links can vary significantly in the same environment, LOS links possess similar features which can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

Empirical modelling of dust storm path attenuation for 5G mmWave

The rapid evolution of communication technologies, particularly the emergence of 5G mm-wave networks, presents unprecedented challenges, particularly in regions prone to dust and sandstorms. These environmental factors can significantly impact 5G signals by attenuating radio waves, leading to signal degradation. This paper investigates the complex interplay between dust storms and 5G mm-wave wireless communication, offering a novel and comprehensive analysis that extends beyond existing models. Our study investigates into the impact of dust storms on 5G mm-wave wireless communication, specifically focusing on signal attenuations under both non-line of sight (NLOS) and line of sight (LOS) conditions. Through the utilize of the NYUSIM channel simulator and exploration of parameters such as dust particle size, storm duration, and environmental factors, we present a detailed numerical results. In the NLOS scenario, path loss measurements record substantial values, highlighting the significant impact of dust storms across different mm-wave frequencies. Conversely, in the LOS scenario, our findings reveal distinct patterns of path loss and shadow fading, shedding light on the complex interaction between dust storms and signal propagation. This research marks a significant advancement in the field, providing a quantitative foundation for addressing dust-induced attenuation in 5G mm-wave communication. By emphasizing novel research methodologies and innovative ideas, our study contributes to a deeper understanding of the challenges posed by dust storms in 5G mm-wave wireless communication systems, paving the way for more effective mitigation strategies and network optimization techniques.

Implicit unscented particle filter based indoor fusion positioning algorithms for sensor networks

Combining inertial navigation system (INS) and ultra-wideband (UWB) technologies can effectively compensate for their respective shortcomings, thus significantly enhancing the accuracy of indoor positioning systems. However, in the process of fusing these two technologies, signal fading under non-line-of-sight (NLOS) conditions, multipath effects, and errors accumulated by the INS over a long period of time are still key issues that need to be addressed. To cope with these challenges, a new fusion localization algorithm is proposed in this study. The algorithm employs a combination of fuzzy C-mean (FCM) and K-Medoids algorithms for UWB for position computation on the one hand, and an Implicit Unscented Particle Filter (IUPF)-enhanced INS for navigation information processing on the other. In addition, based on the INS error equation, this algorithm realizes the effective fusion of UWB and INS positioning information through the Minimum Error Entropy Extended Kalman Filter (MEE-EKF) technique. This integrated approach significantly improves the accuracy and stability when dealing with the localization problem in complex indoor environments. After simulation experiments under different noise conditions and real environment experiments, the algorithm proposed in this study shows significant advantages in terms of localization performance over the traditional UWB/INS localization methods in recent years. In real experiments, the algorithm achieves an average of 36.15% improvement in positioning accuracy.

Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

Enhancing the spatial resolution of time-of-flight based non-line-of-sight imaging via instrument response function deconvolution.

Non-line-of-sight (NLOS) imaging retrieves the hidden scenes by utilizing the signals indirectly reflected by the relay wall. Benefiting from the picosecond-level timing accuracy, time-correlated single photon counting (TCSPC) based NLOS imaging can achieve theoretical spatial resolutions up to millimeter level. However, in practical applications, the total temporal resolution (also known as total time jitter, TTJ) of most current TCSPC systems exceeds hundreds of picoseconds due to the combined effects of multiple electronic devices, which restricts the underlying spatial resolution of NLOS imaging. In this paper, an instrument response function deconvolution (IRF-DC) method is proposed to overcome the constraints of a TCSPC system's TTJ on the spatial resolution of NLOS imaging. Specifically, we model the transient measurements as Poisson convolution process with the normalized IRF as convolution kernel, and solve the inverse problem with iterative deconvolution algorithm, which significantly improves the spatial resolution of NLOS imaging after reconstruction. Numerical simulations show that the IRF-DC facilitates light-cone transform and frequency-wavenumber migration solver to achieve successful reconstruction even when the system's TTJ reaches 1200 ps, which is equivalent to what was previously possible when TTJ was about 200ps. In addition, the IRF-DC produces satisfactory reconstruction outcomes when the signal-to-noise ratio (SNR) is low. Furthermore, the effectiveness of the proposed method has also been experimentally verified. The proposed IRF-DC method is highly applicable and efficient, which may promote the development of high-resolution NLOS imaging.

LoRa Based IoT Enabled Sensor Networks for Plantations

The integration of Wireless Sensor Network (WSN) technology with the Internet of Things (IoT) in agriculture plays a pivotal role in addressing a range of challenges and constraints faced by the sector, encompassing issues such as labour shortages, suboptimal farm management practises, and unpredictable weather conditions. In response to these pressing concerns, this study focuses on the development of WSN with IoT for agriculture, employing a spread spectrum modulation technique named the Long Range (LoRa) module. By leveraging LoRa devices and wireless radio frequencies, this technology serves as a versatile platform for delivering wireless, long-range, and energy-efficient communication to support small and medium-sized agricultural operations. These operations often lack adequate technological assistance due to factors such as limited expertise and the high costs associated with cutting-edge agricultural technology. The research undertakes a comprehensive exploration, involving LoRa parameter testing, Line-of-Sight evaluations, Non-Line-Of-Sight (NLOS) simulations, and sensor calibration, to assess the efficacy of LoRa-based IoT-enabled sensor networks within plantation environments. The overarching objective of this research endeavour is to provide valuable insights that contribute to the optimisation of agricultural practises through streamlined IoT solutions. By implementing practical and cost-effective strategies, the local agricultural sector stands poised to achieve seamless strides in both sustainable and efficient food production.

Research on indoor positioning system algorithm based on UWB technology

Ultra wideband (UWB) indoor positioning technology is attracting significant attention in the present realm of indoor wireless positioning technology due to its numerous advantages, including low energy consumption, exceptional wireless transmission rates, remarkable resolution, and powerful anti-interference capabilities.This paper applies the time of arrival (TOA) location technique, derived from the research on UWB location method, to calculate the distance between the base station and the tag using the signal flight time. By incorporating the bilateral two-way ranging algorithm, the system has been fine-tuned to lower the precision demands on the hardware.A novel positioning algorithm is introduced, utilizing both C-Taylor and adaptive robust Kalman filter, which is based on the Time of Arrival (TOA) method. This algorithm aims to address the limitations of Chan and Taylor algorithms and minimize the non-line-of-sight (NLOS) error. This algorithm has excellent theoretical research value and practical application value.

A recognition algorithm for NLOS-anchors in wireless location applications

In recent years, China's technological level has undergone rapid development, and technologies such as wireless local area networks and wireless sensor networks have also undergone rapid development in this situation. Under the influence of developed networks, many high-tech items are applied in our daily lives, such as intelligent transportation, remote control and monitoring, intelligent household appliances, and so on. In this environment, internet technology is rapidly developing and widely applied in various fields, making huge contributions to positioning technology and monitoring technology. The demand for high-precision, low-cost, strong applicability, and simple operation scientific technology in various industries is constantly increasing. Ultra wideband technology has played a significant role in the field of indoor positioning due to its unique advantages. However, in complex environments, the propagation of broadband signals is easily affected by obstacles, which weakens the signal received and seriously affects indoor positioning accuracy. As people gradually move into cities, the increase in population has caused increasingly serious traffic congestion. In order to solve the positioning problem of urban people, positioning industries that serve people have gradually emerged in the public's career, which has led to an increasing demand for accurate positioning. The complex environment of cities and the complex environment where people live can have a serious impact on positioning technology, leading to continuous deterioration of positioning accuracy. Non-Line of Sight (NLOS) is an important factor affecting positioning accuracy. So, this article conducts a thorough study on wireless local area networks and wireless sensor networks, fully improving the development of wireless positioning, and conducting a thorough study on the algorithm of NLOS- anchor.

Cohesive framework for non-line-of-sight imaging based on Dirac notation.

The non-line-of-sight (NLOS) imaging field encompasses both experimental and computational frameworks that focus on imaging elements that are out of the direct line-of-sight, for example, imaging elements that are around a corner. Current NLOS imaging methods offer a compromise between accuracy and reconstruction time as experimental setups have become more reliable, faster, and more accurate. However, all these imaging methods implement different assumptions and light transport models that are only valid under particular circumstances. This paper lays down the foundation for a cohesive theoretical framework which provides insights about the limitations and virtues of existing approaches in a rigorous mathematical manner. In particular, we adopt Dirac notation and concepts borrowed from quantum mechanics to define a set of simple equations that enable: i) the derivation of other NLOS imaging methods from such single equation (we provide examples of the three most used frameworks in NLOS imaging: back-propagation, phasor fields, and f-k migration); ii) the demonstration that the Rayleigh-Sommerfeld diffraction operator is the propagation operator for wave-based imaging methods; and iii) the demonstration that back-propagation and wave-based imaging formulations are equivalent since, as we show, propagation operators are unitary. We expect that our proposed framework will deepen our understanding of the NLOS field and expand its utility in practical cases by providing a cohesive intuition on how to image complex NLOS scenes independently of the underlying reconstruction method.