Line Of Sight Signal Research Articles

Behind Corner Targets Location Using Small Aperture Millimeter Wave Radar in NLOS Urban Environment

It is often difficult to detect the targets of interest via the line-of-sight (LOS) signals in the urban environment, since the LOS signals might be not available because of the presence of the walls. This article considers the target positioning problem via non-LOS signals using the single-input multiple-output small aperture millimeter wave radar. Assume that a target is located around a building corner and the geometric structure is known a priori . The phase comparison based algorithm is proposed, by exploiting the multipath propagation of the electromagnetic wave and the phase differences of the echoes among multiple channels. Finally, we validate the proposed algorithm via real data.

A gradient boosting decision tree based GPS signal reception classification algorithm

In urban areas, GPS signals are often reflected or blocked by buildings, which causes multipath effects and non-line-of-sight (NLOS) reception respectively consequently degrading GPS positioning performance. While improved receiver design can reduce the effect of multipath to some extent, it cannot deal with NLOS. Modelling methods based on measurements have shown promise to reduce the effect of NLOS signal reception. However, this depends on their ability to accurately and reliably classify line-of-sight (LOS), multipath and NLOS signals. The traditional method is based on one feature using signal strength as measured by the carrier to noise ratio, C/N0. However, this feature is ineffective in capturing the characteristics of multipath and NLOS in all environments. In this paper, to improve the accuracy of signal reception classification, we are using the three features of C/N0, pseudorange residuals and satellite elevation angle with a gradient boosting decision tree (GBDT) based classification algorithm. Experiments are carried out to compare the proposed algorithm with classifiers based on decision tree, distance weighted k-nearest neighbour (KNN) and the adaptive network-based fuzzy inference system (ANFIS). Test results from static receivers in urban environments, show that the GBDT based algorithm achieves a classification accuracy of 100%, 82% and 86% for LOS, multipath and NLOS signals, respectively. This is superior to the other three algorithms with the corresponding results of 100%, 82% and 84% for the Distance-Weighted KNN, 99%, 70% and 65% for the ANFIS and 98%, 35% and 95% for the traditional decision tree. With the NLOS detection and exclusion, the proposed GBDT with multi-feature based method can provide a positioning accuracy improvement of 34.1% compared to the traditional C/N0 based method.

Line of Sight Component Identification and Positioning in Single Frequency Networks Under Multipath Propagation

Broadcasting signals have been recognized as promising candidates for navigation and positioning needs. However, in most single frequency broadcasting networks, the transmitter confusion problem poses a huge obstacle. Not holding the transmitter identities, the receiver cannot determine the distance to the transmitters, let alone its own position. What’s worse, considering the existence of multipath propagation, the receiver is unable to distinguish line of sight (LOS) components from non line of sight components, which makes positioning even harder. In this paper, a two-step method is proposed to solve the positioning problem in single frequency networks (SFNs) under multipath propagation yet with direct path. First, a probability approach is proposed to identify all LOS components iteratively. Then, it is proved in this paper that a receiver can determine its position when receiving LOS signals from at least five transmitters without the knowledge of transmitter identities. Detailed analysis is carried out in this paper to derive error bounds and performance limits from different perspectives. Simulation results show that the proposed algorithm can achieve high accuracy of positioning across multiple environments. Besides, the proposed methodology is general, which can be used in any SFN or other wireless networks for positioning needs.

Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems

Identification of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband localization systems. In the identification, supervised machine learning is often used, but it requires exorbitant efforts to maintain and label the LOS and NLOS database. In this paper, we apply unsupervised machine learning approach called “expectation maximization for Gaussian mixture models” to classify LOS and NLOS components. The key advantage of applying unsupervised machine learning is that it does not require any rigorous and explicit labeling of the database at a certain location. The simulation results demonstrate that by using the proposed algorithm, LOS and NLOS signals can be classified with 86.50% correct rate, 12.70% false negative, and 0.8% false positive rate. We also compare the proposed algorithm with the existing cutting-edge supervised machine learning algorithms in terms of computational complexity and signals' classification performance.

LMMSE Receivers in Uplink Massive MIMO Systems With Correlated Rician Fading

We carry out a theoretical analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician fading, using two processing approaches. First, we examine the linear-minimum-mean-square-error receiver under training-based imperfect channel estimates. Second, we propose a statistical combining technique that is more suitable in environments with strong line-of-sight (LoS) components. We derive closed-form asymptotic approximations of the UL spectral efficiency (SE) attained by each combining scheme in single and multi-cell settings, as a function of the system parameters. These expressions are insightful in how different factors such as LoS propagation conditions and pilot contamination impact the overall system performance. Furthermore, they are exploited to determine the optimal number of training symbols, which is shown to be of significant interest at low Rician factors. The study and numerical results substantiate that stronger LoS signals lead to better performances, and under such conditions, the statistical combining entails higher SE gains than the conventional receiver.

A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning

Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB). In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years. Therefore, the identification of NLOS signals is of great significance in UWB positioning. The traditional machine learning method is based on the assumption that the number of samples of the line-of-sight (LOS) and NLOS signals are balanced. However, in reality, the number of LOS signals in UWB positioning is much larger than the NLOS signals. So the samples are characterized by class-imbalance. In response to this fact, we applied a fast imbalanced binary classification method based on moments (MIBC) to identify NLOS signals. The method uses the mean and covariance of the two first moments of the LOS signal samples to represent its probability distribution and then uses the probability distribution and all a small amount of NLOS signal samples to establish a model. This method does not depend on the number of LOS signals and is suitable for dealing with the problem of classification of the imbalance between the number of LOS and NLOS signals. Numerical simulations also verify that the method has better performance than LS-SVM and SVDD.

Dual polarization beamforming algorithm for multipath mitigation in GNSS

This paper treats the problem of line-of-sight (LOS) parameter estimation in strong multipath environments. In the case of highly temporally and spatially correlated LOS and multipath signals, such as urban canyons, common multipath mitigation methods are highly degraded, as signal separation cannot be performed in the spatio temporal domain. In this case, we exploit the LOS and multipath polarization diversity to decouple the signals using an antenna array with right-hand-circular polarization (RHCP) and left-hand-circular polarization (LHCP) feeds. The multipath direction- of-arrival (DOA) and polarization coefficients can effectively be estimated from the LHCP spatial covariance matrix. The LOS DOA can be estimated from the RHCP spatial covariance matrix. The spatial covariance matrices are calculated from the outputs of a matched correlator bank. The DOA and polarization estimates are used to implement a dual polarization beamformer, which maximizes the LOS energy and suppresses multipath energy over both polarizations. The LOS time-delay is estimated from the beamformer output with a maximum-likelihood estimator with a significantly reduced number of parameters and computational complexity in comparison to a full model estimator. Simulation results for GPS show that the proposed dual polarization beamforming algorithm performs better than an equivalent single-polarization beamformer in a dense multipath environment.

Mitigating Multipath Bias Using a Dual-Polarization Antenna: Theoretical Performance, Algorithm Design, and Simulation.

It is well known that multipath effect remains a dominant error source that affects the positioning accuracy of Global Navigation Satellite System (GNSS) receivers. Significant efforts have been made by researchers and receiver manufacturers to mitigate multipath error in the past decades. Recently, a multipath mitigation technique using dual-polarization antennas has become a research hotspot for it provides another degree of freedom to distinguish the line-of-sight (LOS) signal from the LOS and multipath composite signal without extensively increasing the complexity of the receiver. Numbers of multipath mitigation techniques using dual-polarization antennas have been proposed and all of them report performance improvement over the single-polarization methods. However, due to the unpredictability of multipath, multipath mitigation techniques based on dual-polarization are not always effective while few studies discuss the condition under which the multipath mitigation using a dual-polarization antenna can outperform that using a single-polarization antenna, which is a fundamental question for dual-polarization multipath mitigation (DPMM) and the design of multipath mitigation algorithms. In this paper we analyze the characteristics of the signal received by a dual-polarization antenna and use the maximum likelihood estimation (MLE) to assess the theoretical performance of DPMM in different received signal cases. Based on the assessment we answer this fundamental question and find the dual-polarization antenna’s capability in mitigating short delay multipath—the most challenging one among all types of multipath for the majority of the multipath mitigation techniques. Considering these effective conditions, we propose a dual-polarization sequential iterative maximum likelihood estimation (DP-SIMLE) algorithm for DPMM. The simulation results verify our theory and show superior performance of the proposed DP-SIMLE algorithm over the traditional one using only an RHCP antenna.

Multipath detection based on single orthogonal dual linear polarized GNSS antenna

Based on the polarization difference between the multipath and the line-of-sight (LOS) signal, a method for multipath detection using a single antenna is proposed. The antenna has two channels to receive two orthogonal linear polarized components of the multipath and LOS signal, respectively. A hypothetical model of the antenna is employed such that the antenna patterns of the channels are assumed identical regarding amplitude and phase and are independent of azimuth. The antenna gain in the direction below the local horizon is assumed to be larger than in the direction toward LOS signals. Parallel cross-cancellation is used to remove the LOS signal from the received signals based on the magnitude and phase difference between the two orthogonal components. Then the residual signals are processed by a conventional digital processor of global navigation satellite system. The multipath can be detected by parallel cross-cancellation in the receiver in real time. The proposed method makes use of the polarization and spatial information of the multipath and LOS signal, and can detect short-delay multipath.

NLOS Signal Detection Based on Single Orthogonal Dual-Polarized GNSS Antenna

Nowadays users have a high demand for the accuracy of position and velocity, but errors caused by non-line-of-sight (NLOS) signals cannot be removed effectively. Since the GNSS signal is right-hand circular polarized (RHCP), the axial ratio of the strong NLOS signal is larger than that of the Line-of-Sight (LOS) signal. Based on the difference of the axial ratio, a method for NLOS signal detection using single orthogonal dual-polarized antenna is proposed. The antenna has two channels to receive two orthogonal linear polarized components of the incoming signals. Parallel cross-cancellation is used to remove the LOS signal while maintaining most of the NLOS signals from the receiving signals. The residual NLOS signals are then detected by conventional GNSS digital processor in real time without any prior knowledge of their characteristics. The proposed method makes use of the polarization and spatial information and can detect long delay NLOS signal by miniature and inexpensive receiver GNSS. The effectiveness of the proposed method is confirmed by simulation data.

5-GHz Vehicle-to-Vehicle Channel Characterization for Example Overpass Channels

The overpass is a special over-road structure for vehicular travel, constituting one type of roadway intersection. Real-time communications between on- and under-overpass vehicles can contribute to effective vehicle-to-vehicle (V2V) communications, including, for example, optimal route selection and overpass accident warning messaging. Ensuring effective communication requires a quantitative characterization of the overpass propagation channel. In this paper, we provide measurement and analytical results for V2V propagation path loss and root-mean-square delay spread, and from these results, we develop tapped-delay line channel models that are applicable to the 5-GHz band for two example overpasses. These two example overpasses are termed i) one-lane metal-bottom overpass and ii) two-lane metal-bottom overpass. Due to the unique structure of the overpasses, we divide the radio propagation space around the overpass into four different areas: a two-ray area, a short-term partial-shadowing area, a (full) shadowing area, and a long-term partial-shadowing area. In the two-ray area, a line-of-sight (LOS) path and a ground-reflected path are the dominant propagation mechanisms, whereas in the other areas, the overpass body (floor, walls, and columns) attenuates the LOS signal. The accuracy of our measurement results and the developed channel model are verified by a geometry-based stochastic channel modeling approach, in which the height dimension is first introduced to characterize the diffraction phenomenon in V2V communication. The actual measurement and simulation results show good consistency.

Measurement Error Observer-Based IMM Filtering for Mobile Node Localization Using WLAN RSSI Measurement

This paper proposes a mobile node (MN) localization technique using received signal strength indicator (RSSI) data of wireless local area network. Range-domain measurement converted from the raw RSSI data can be used for localizing the MN. In indoor environments, however, the measurements may contain large errors caused by wall penetrating line-of sight (LOS) signals and non-LOS signals. The measurement errors cause large localization error. In this paper, a measurement error observer (MEO) is newly developed to mitigate the localization error. In addition, the MEO is integrated with an interacting multiple model (IMM) filter to take the flexible dynamic model of the MN into consideration. The purpose of the developed MEO-based IMM filter is to decouple the measurement errors from the localization and to adapt the filter for the flexible dynamics of the MN. Through comparative simulation and experimental tests, the performance of the developed filter is evaluated.

Data Fusion of Time Stamps and Transmitted Data for Unsynchronized Beacons

Unsynchronized localization systems based on the measurement of time (difference) of arrival require reliable time stamps of the received signal. Noise, frequency shifts, and echoes disturb the signal and induce measurement errors of the time stamp, which leads to localization errors. Furthermore, the line of sight (LOS) signal has to be distinguished from the echoes to avoid false signal tracking. The proposed method combines the information of an ultrasound transmission with the measured time stamp and estimates the identifier. In our approach, the ultrasound transmission system uses phase-shift keying to modulate the signal. The received symbols and the time stamps are tracked and fused by the Kalman filter to increase the signal-to-noise ratio of the fused symbols and improve the validity of the decoding. Hence, the bias of the received symbols is tracked and the tracking allows to distinguish between the LOS signal and the echoes. As a result, the data fusion reduces the packet error rate from 70% at a distance of 21 m to 4.5%. Moreover, the median error of the localization is reduced from 7 to 4.6 cm.

A Statistical Model for Shadowed Body-Centric Communications Channels: Theory and Validation

This paper presents a new statistical signal reception model for shadowed body-centric communications channels. In this model, the potential clustering of multipath components is considered alongside the presence of elective dominant signal components. As typically occurs in body-centric communications channels, the dominant or line-of-sight (LOS) components are shadowed by body matter situated in the path trajectory. This situation may be further exacerbated due to physiological and biomechanical movements of the body. In the proposed model, the resultant dominant component which is formed by the phasor addition of these leading contributions is assumed to follow a lognormal distribution. A wide range of measured and simulated shadowed body-centric channels considering on-body, off-body and body-to-body communications are used to validate the model. During the course of the validation experiments, it was found that, even for environments devoid of multipath or specular reflections generated by the local surroundings, a noticeable resultant dominant component can still exist in body-centric channels where the user's body shadows the direct LOS signal path between the transmitter and the receiver.

Bayesian-based MEDLL for the GPS Signal Tracking

In the satellite navigation systems, distortion of a received signal correlation function, due to the multipath propagation, can gravely degrade position estimation. The positioning accuracy is strongly affected by the quality of the received signal delay estimations. In the paper we propose a Bayesian-based extension of the MEDLL algorithm capable of estimating the parameters of the line-of-sight (LOS) signal and the multipath signals. The new algorithm takes into account statistical properties of the LOS signal and the reflected signals to find a Bayesian solution for the parameters. DOI: http://dx.doi.org/10.5755/j01.eee.18.9.2809

AOD estimation in WSN localization system with synthetic aperture technique

Angel of departure (AOD) based distributed localization scheme in wireless sensor networks allows the single antenna sensor node to estimate its own AOD information by an equivalent antenna array for further position calculation. However, the condition that the number of antennas at the multi-antenna agent/anchor node should be larger than the number of multipath signal components (MPCs) constrains its actual application. In this paper, we provide a more practical approach to achieve AOD information estimation under multipath environment. Given that the sensor node can not identify the line of sight (LOS) signal component due to the insufficient degree of freedom, which is determined by the above limiting condition, we introduce a synthetic aperture technique with the aid of a fast moving multi-antenna agent/anchor node. This operation greatly enlarges the degree of freedom of the equivalent antenna array constructed at sensor node end but without requiring other extra physical antennas equipped at agent/anchor node or sensor node end, which means that it relaxes the limiting condition. Therefore, the AOD information of LOS signal component can be estimated and extracted successfully in complex multipath environment. Theoretical analysis proves the effectiveness of our proposed synthetic aperture technique and numerical simulations show great performance improvement.

A Bayesian Approach to Multipath Mitigation in GNSS Receivers

Multipath is known to be one of the most dominant sources of accuracy degradation in satellite-based navigation systems. Multipath may cause biased position estimates that could jeopardize high-precision applications. This paper considers the problem of tracking the time-variant synchronization parameters of both the line-of-sight signal (LOSS) and its multipath replicas. In particular, the proposed algorithm tracks time-delays, amplitudes, phases and proposes a procedure to extract Doppler shifts from complex amplitudes. However, the interest is focused on LOSS time-delay estimates, since those provide the means to compute user's position. The undertaken Bayesian approach is implemented by a particle filter. The selection of the importance density function, from which particles are generated, is performed using a Gaussian approximation of the posterior function. This selection provides a particle generating function close to the optimal, which yields to an efficient usage of particles. The complex-linear part of the model, i.e., complex amplitudes, is tackled by a Rao-Blackwellization procedure that implements a complex Kalman filter for each generated particle, thus reducing the computational load. Computer simulation results are compared to other Bayesian filtering alternatives (namely, the extended Kalman filter, the unscented Kalman filter and the sequential importance resampling algorithms) and the posterior Cramer-Rao bound.

A Novel Spatially Correlated Multiuser MIMO Channel Modeling: Impact of Surface Roughness

A novel approach for modeling multiuser multiple-input and multiple-output (MIMO) channels is proposed. Here, the scatterers are modeled as random rough surfaces: Any point on rough surfaces scatters the incident wave into any given direction with certain probability. This leads to correlation across different spatially distributed users. The paper derives closed form expressions for the intra-user (point-to-point) and inter-user (multiuser) correlation of pair-wise channel coefficients. It is observed that the distance of users relative to the scatterer surfaces as well as the degree of roughness impact the correlation region. The technique is applied to both non-line-of-sight (NLOS) and line-of-sight (LOS) scenarios. It is observed that LOS signals drastically boost the correlation of the channel coefficients.

DoA Estimation of Line of Sight Signal in Multipath Channel for GNSS Receiver

The GNSS receivers suffer from the multipath interference which is highly correlated with the line of sight (LoS) signal. Such interference results in tracking and ranging errors. In this paper, we propose a novel algorithm that can estimate the direction of arrival (DoA) of the LoS signal in the presence of highly correlated multipath interference. The proposed algorithm combines the matching pursuits algorithm for multipath estimation and the minimum norm algorithm for DoA estimation. An efficient combination of two algorithms yields reliable estimates of the DoA of LoS signal as demonstrated by computer simulations.

LOCALIZATION OF OMNI-DIRECTIONAL MOBILE DEVICE IN MULTIPATH ENVIRONMENTS

This paper presents a comprehensive Non Line of Sight (NLOS) localization scheme in a multipath environment where the scatterers with smooth surfaces are aligned parallel or perpendicular to each other. It leverages on the estimation of Angle of Arrival (AOA) and Time of Arrival (TOA) of the omni-directional mobile device's signal received at the reference devices. Unlike the conventional Line of Sight (LOS) localization schemes that rely on the various mitigation techniques to mitigate the multipaths that are mistaken as the LOS signal, our proposed two step localization scheme not only utilizes the LOS path but also any one bound scattering NLOS multipath arriving at the reference devices for localization. Channel experiment coupled with simulation results in a typical multipath environment has demonstrated that our proposed localization scheme outperforms the conventional localization schemes that are coupled with their own mitigation techniques. Robustness in performance of our proposed localization scheme towards different scatterers' orientation where they are not aligned parallel or perpendicular to each other are also investigated.