Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

The fifth generation (5G) system using millimeter waves is considered for application to high traffic areas with a dense population of pedestrians. In such an environment, the effects of shadowing and scattering of radio waves by human bodies (HBs) on propagation channels cannot be ignored. In this paper, we clarify based on measurement the characteristics of waves scattered by the HB for typical non-line-of-sight scenarios in street canyon environments. In these scenarios, there are street intersections with pedestrians, and the angles that are formed by the transmission point, HB, and reception point are nearly equal to 90 degrees. We use a wide-band channel sounder for the 67-GHz band with a 1-GHz bandwidth and horn antennas in the measurements. The distance parameter between antennas and the HB is changed in the measurements. Moreover, the direction of the HB is changed from 0 to 360 degrees. The evaluation results show that the radar cross section (RCS) of the HB fluctuates randomly over the range of approximately 20dB. Moreover, the distribution of the RCS of the HB is a Gaussian distribution with a mean value of -9.4dBsm and the standard deviation of 4.2dBsm.

In this paper, we applied cavity resonator wireless power transfer (CR WPT) to an enclosed space with scatterers and revealed that high transfer efficiency at line-of-sight (LOS) and non-line-of-sight (NLOS) position in the power transmitter can be achieved by this method. In addition, we propose a method for limiting the wireless power transfer space utilizing metal mesh and show its effectiveness by experiment. First, we confirm that the constructed experimental model is working as a cavity resonator by theoretical formula and electromagnetic field analysis. Next, we calculate the maximum power transfer efficiency using a model including a plurality of scatterers by installing a power receiver at LOS and NLOS positions in the power transmitter, and it was confirmed that transfer efficiency of 30% or more could be expected even at the NLOS position. Then, we measured the frequency characteristics of a model in which one surface of the outer wall was replaced with a metal mesh, and it was clarified that the characteristics hardly changed in the power transfer frequency band. Finally, we confirmed that simultaneous communication can be performed with driving of the battery-less sensor by CR WPT, and clarify effectiveness of the proposed method.

This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

In indoor localization using sensor networks, performance improvements are required for non-line-of-sight (NLOS) environments in which the estimation error is high. NLOS mitigation schemes involve the detection and elimination of the NLOS measurements. The iterative minimum residual (IMR) scheme, which is often applied to the localization scheme using the time of arrival (TOA), is commonly employed for this purpose. The IMR scheme is a low-complexity scheme and its NLOS detection performance is relatively high. However, when there are many NLOS nodes in a sensor field, the NLOS detection error of the IMR scheme increases and the estimation accuracy deteriorates. Therefore, we propose a new scheme that exploits coarse NLOS detection based on stochastic characteristics prior to the application of the IMR scheme to improve the localization accuracy. Improved performances were confirmed in two NLOS channel models by performing numerical simulations.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several hybrid methods that utilized time of arrival (TOA) at seven base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are easier by using linear LOP to determine the MS. In addition, the proposed methods can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) and the previous proposed methods employing circular LOP.

Highly accurate pedestrian position information is required in many applications, especially in automatic driving system. Global Positioning System (GPS) developed by American has proven itself reliability in most of the environments. Unfortunately, urban areas contain the signal reflection, known as multipath and non-line-of-sight (NLOS) effects. In addition, the lake of line-of-sight (LOS) satellites caused by the blockage of skyscrapers also severely degrades the accuracy and availability of the GPS positioning. To solve these problems, a solution that interoperated several Global Navigation Satellite Systems (GNSSs) is proposed. However, the actual difficulty of satellite positioning in urban area is the distorted satellite distribution. This paper proposes a GPS with 3D map ray tracing positioning method to conquer the difficulty. The proposed method takes the advantage of the non-LOS (NLOS) and uses it as an additional measurement. Significantly, these measurements are sourced from the satellites that should be blocked. Thus, the dilution of precision (DOP) can be greatly improved. To verify the performance of the proposed method, real data is collected at Tokyo urban area. This paper compares the performance of GPS/GLONASS and the proposed GPS with 3D map ray tracing methods. The results reveals the proposed method is capable of identifying which side of street the pedestrian stands and the GPS+GLONASS method is not.

For estimating user's location, Global Navigation Satellite System (GNSS) is very useful. Especially, Global Positioning System (GPS) by USA is very popular. A GPS receiver needs multiple satellites (usually 4 and more satellites). Propagation to the satellites needs line-of-sight. However, in urban area, there are many buildings. Received signals tend to become bad quality. Such signals are often called as non-line-of-sight (NLOS) or multipath signals. The problem is that the receiver cannot get line-of-sight signals from adequate number of the satellites coinstantaneously. This case leads to degradation of estimation quality or impossibility of estimation. In this paper, we will introduce a novel estimation algorithm, which can estimate own position with as low number of satellites as possible. The proposal achieves the estimation by only two satellites. The proposal also uses a traveling distance sensor which is often equipped on vehicles. By recorded satellite data, we will confirm our effectiveness.

In this paper, we present an efficient time-of-arrival (TOA)-based localization method for wireless sensor networks. The goal of a localization system is to accurately estimate the geographic location of a wireless device. In real wireless sensor networks, accurately estimating mobile device location is difficult because of the presence of various errors. Therefore, localization methods have been studied in recent years. In indoor environments, the accuracy of wireless localization systems is affected by non-line-of-sight (NLOS) errors. The presence of NLOS errors degrades the performance of wireless localization systems. In order to effectively estimate the location of the mobile device, NLOS errors should be recognized and mitigated in indoor environments. In the TOA-based ranging method, the distance between the two wireless devices can be computed by multiplying a signal's propagation delay time by the speed of light. TOA-based localization measures the distance between the mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors of the measured distance between the i-th BS and the MS is different due to dissimilar obstacles in the direct signal path between the two nodes. In order to accurately estimate the location in a TOA-based localization system, an optimized localization algorithm that selects three measured distances with fewer NLOS errors is necessary. We present an efficient TOA-based localization scheme that combines three selected BSs in wireless sensor networks. This localization scheme yields improved localization performance in wireless sensor networks. In this paper, performance tests are performed, and the simulation results are verified through comparisons between various localization methods and the proposed method. As a result, proposed localization scheme using BS selection achieves remarkably better localization performance than the conventional methods. This is verified by experiments in real environments, and demonstrates a performance analysis in NLOS environments. By using BS selection, we will show an efficient and effective TOA-based localization scheme in wireless sensor networks.

Sensor networks, in which many small terminals are wirelessly connected, have recently received considerable interest according to the development of wireless technology and electronic circuit. To provide advanced applications and services by the sensor networks, data collection including node location is essential. Hence the location estimation is important and many localization schemes have been proposed. Time of arrival (TOA) localization is one of the popular schemes because of its high estimation accuracy in ultra wide-band (UWB) sensor networks. However, a non-line-of-sight (NLOS) environment between the target and the anchor nodes causes a serious estimation error because the time is delayed more than its true one. Thus, the NLOS nodes should be detected and eliminated for estimation. As a well-known NLOS detection scheme, an iterative minimum residual (IMR) scheme which has low calculation complexity is used for detection. However, the detection error exists in IMR scheme due to the measurement error. Therefore, in this paper, we propose a new IMR-based NLOS detection scheme and show its performance improvement by computer simulations.

In the MIMO-OFDM multiple access channel (MIMO-OFDM-MAC) uplink scenario, the base station decides the uplink parameters for multiple users based on channel state information (CSI) from each user in the system. The performance of MIMO-OFDM-MAC systems can be significantly improved by using an adaptive transmission and resource allocation schemes which consider the correlation effect of line of sight (LOS) and non line of sight (NLOS) channel conditions for different users in the system. A lot of papers have been published on resource allocation schemes for MIMO-OFDM systems. However, most of these resource allocation schemes have been considered for MIMO-OFDMA systems, where users are separated in the frequency domain and each user uses the same uplink and downlink channels in the same channel conditions. On the other hand, in the mulituser MIMO-OFDM systems, more than one user can be assigned the same frequency and channel conditions for the MIMO-OFDM broadcast channel (downlink) and MIMO-OFDM-MAC channel (uplink) are not the same. Therefore, the same resource allocation schemes for the conventional MIMO-OFDM systems can not be applied to multiuser MIMO-OFDM systems with different uplink and downlink channel conditions. Until now, most of the resource allocation schemes have been considered only for downlink MIMO-OFDM broadcast (MIMO-OFDM-BC) channel and very few papers tackle the fairness among users. Moreover, no paper considers a scheme to realize proportional data rate fairness among users in the MIMO-OFDM-MAC condition. In this paper, we propose a proportional data rate fairness resource allocation scheme with adaptive bit loading for MIMO-ODFM-MAC systems by considering the correlation effects of LOS and NLOS channel conditions in both spatial and frequency domains. Computer simulation results show that the proposed scheme can give larger system capacity while maintaining the proportional data rate fairness requirements among users in the system under the constraint of total transmit power and predetermined target BER.

To overcome the shortcomings of conventional cellular positioning, a novel cooperative location algorithm that uses the available peer-to-peer communication between the mobile terminals (MTs) is proposed. The main idea behind the proposed approach is to incorporate the long- and short-range location information to improve the estimation of the MT's coordinates. Since short-range communications among MTs are characterized by high line-of-sight (LOS) probability, an improved spring-model-based cooperative location method can be exploited to provide low-cost improvement for cellular-based location in the non-line-of-sight (NLOS) environments.

To achieve more accurate measurements of the mobile station (MS) location, it is possible to integrate many kinds of measurements. In this paper we proposed several simpler methods that utilized time of arrival (TOA) at three base stations (BSs) and the angle of arrival (AOA) information at the serving BS to give location estimation of the MS in non-line-of-sight (NLOS) environments. From the viewpoint of geometric approach, for each a TOA value measured at any BS, one can generate a circle. Rather than applying the nonlinear circular lines of position (LOP), the proposed methods are much easier by using linear LOP to determine the MS. Numerical results demonstrate that the calculation time of using linear LOP is much less than employing circular LOP. Although the location precision of using linear LOP is only reduced slightly. However, the proposed efficient methods by using linear LOP can still provide precise solution of MS location and reduce the computational effort greatly. In addition, the proposed methods with less effort can mitigate the NLOS effect, simply by applying the weighted sum of the intersections between different linear LOP and the AOA line, without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods can always yield superior performance in comparison with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

Intelligent transportation system (ITS) makes use of vehicle position to decrease the heavy traffic and improve service reliability of public transportation system. Many existing systems, such as global positioning system (GPS) and cellular communication systems, can be used to estimate vehicle location. The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. The non-line-of-sight (NLOS) problem is the most crucial factor that it causes large measured error. In this paper, we present a novel positioning algorithm based on genetic algorithm (GA) to locate MS when three BSs are available for location purpose. Recently, GA are widely used as many various optimization problems. The proposed algorithm utilizes the intersections of three time of arrival (TOA) circles based on GA to estimate the MS location. The simulation results show that the proposed algorithms can really improve the location accuracy, even under severe NLOS conditions.

This paper presents a Multiple-Input Multiple-Output (MIMO) propagation model for independent and identically distributed (i.i.d.) channels in the mixture of none-Line-of-Sight (NLOS) and Line-of-Sight (LOS) environments. The derived model enables to evaluate the system statistical characteristics of Signal-to-Noise-Ratio (SNR) for MIMO transmission based on Maximal Ratio Combing (MRC). An application example applying the model in 22 configuration to ITS Inter-Vehicle Communication (IVC) system is introduced. We clarify the effectiveness of the proposed model by comparisons of both computer simulations and measurement results of a field experiment. We also use the model to show the better performance of SNR when applying MIMO to IVC system than SISO and SIMO.

The objective of wireless location is to determine the mobile station (MS) location in a wireless cellular communications system. When signals are propagated through non-line-of-sight (NLOS) paths, the measurements at the base stations (BSs) contain large errors which result in poor detectability of an MS by the surrounding BSs. In those situations, it is necessary to integrate all available heterogeneous measurements to improve location accuracy. This paper presents hybrid methods that combine time of arrival (TOA) at three BSs and angle of arrival (AOA) information at the serving BS to obtain a location estimate for the MS. The proposed methods mitigate the NLOS effect by using the weighted sum of the intersections between three TOA circles and the AOA line without requiring the a priori knowledge of NLOS error statistics. Numerical results show that all positioning methods offer improved estimation accuracy over those which rely on the two circles and two lines. The proposed methods always achieve better location accuracy than the Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) do, regardless of the NLOS error statistics.

In ubiquitous sensor networks, the estimation accuracy of a node location is limited due to the presence of non-line-of-sight (NLOS) paths. To mitigate the NLOS effects, this letter proposes a simple algorithm where NLOS identification is carried out using angle-of-arrival (AOA). Simulation results show that the use of AOA improves NLOS identification rates and location estimation accuracy.

A novel method is proposed to track the position of MS in the mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions in cellular network. A first-order markov model is employed to describe the dynamic transition of LOS/NLOS conditions, which is hidden in the measurement data. This method firstly uses modified EKF banks to jointly estimate both mobile state (position and velocity) and the hidden sight state based on the the data collected by a single BS. A Bayesian data fusion algorithm is then applied to achieve a high estimation accuracy. Simulation results show that the location errors of the proposed method are all significantly smaller than that of the FCC requirement in different LOS/NLOS conditions. In addition, the method is robust in the parameter mismodeling test. Complexity experiments suggest that it supports real-time application. Moreover, this algorithm is flexible enough to support different types of measurement methods and asynchronous or synchronous observations data, which is especially suitable for the future cooperative location systems.

This letter proposes a UWB signaling localization scheme for indoor multipath channel. It demonstrates that the proposed method does not require LOS path (LP) and is suitable for severe non line-of-sight (NLOS) condition. A low-complexity TOA estimation algorithm, the strongest path (SP) detection by convolution, is designed, which is easier to implement than the LP detection since it dispenses with the process of threshold setting. Experiments under NLOS channels in IEEE.802.15.4a are conducted and the localization influences due to the algorithm parameters are discussed. The results prove the feasibility of the proposed localization scheme under the indoor multipath NLOS environment.