Improved selective hybrid RSS/AOA weighting schemes for NLOS localization

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.

Range estimation based on Radio Signal Strength (RSS) has been widely adopted by the indoor localization systems. Many existing works have been devoted to tackle the imprecise and unreliable RSS measurements caused by multipath fading. But there exist only a few works dealing with errors caused by non-line-of-sight (NLOS) radio propagation. In some circumstances, it is common for obstacles (e.g. human movements) to cause NLOS measurements, which could undermine the whole ranging process by introducing significant NLOS errors. In this letter, we propose to use a Gaussian Mixture Model (GMM) to model the distribution of a set of NLOS corrupted range estimations. In the GMM method, the distribution of LOS estimations and the distribution of NLOS estimations are represented by different Gaussian components. Consequently, the ranging quality is improved by employing soft exclusion of those Gaussian components associated with NLOS. An indoor field experiment has been performed to verify the proposed method.

Enhanced 911 is a first line of assistance for practically every emergency situation, and many cell phone users today expect the same results from an emergency call no matter where they are-whether on the side of the road, in the woods, or in a building. It is a vital part of our nation's emergency response and disaster preparedness system. In the context of 911 service, demand for providing reliable and accurate mobile station (MS) location estimation has become a high priority and has gained momentum in recent years. A major challenge in mobile station location estimation is locating an emergency caller within desired accuracy in an adverse environment where non-line-of-site (NLOS) propagation exists. This paper develops a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. A unique feature of this methodology development, compared to other approaches in the literature, is the application of the systems engineering process. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. The methodology consists of two stages. In the first stage, a series of time-of-arrival range measurements are made from each base station (BS) to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. Based on a simulation study, the methodology appears to have the potential to significantly improve the accuracy of location estimates in certain situations.

This paper proposes a novel localization scheme to estimate the location of the mobile device by using single reference device. This scheme makes use of the bidirectional information of the line of sight (LOS) and non line of sight (NLOS) paths to construct the area of possible mobile device (APMD) and line of possible mobile device (LPMD) respectively. The location of the mobile device is determined by using the weighted least squares solution based on the intersection of these APMD and LPMD. Simulation and experimental results demonstrate that the performance of our proposed localization scheme outperforms the existing localization schemes by significant margins.

In this thesis, we cope with the fundamental mathematical and physical problems of the space-time-Doppler UWB system design for fading channels: multi-input multi-output (MIMO) wireless communication with adaptive filtering. The estimation algorithm from equalizer providing an accurate parameters estimate is investigated. UWB radio is a fast emerging technology with uniquely attractively features inviting major advances in wireless communications, networking, radar, imaging, and positioning systems. By its rule-making proposal in 2002, the Federal Communications Commission (FCC) in the United States essentially unleashed huge new bandwidth (3.6-10.6 GHz) at the noise floor, where UWB radios overlaying coexistence RF systems cerate using low-power ultra-short information bearing pulses. Right now, there are three basic types of UWB: multiband OFDM UWB (MB-OFDM UWB), impulse-UWB (I-UWB), and direct sequence UWB (DS-UWB). We focus on I-UWB and MB-OFDM-UWB, and compare their performance. A simple means for spreading the spectrum of low duty cycle pulse trains is time hopping (TH), with pulse position modulation (PPM) for data modulation at the rate of many pulses per data bit. The main contribution of this thesis for I-UWB is the design of leading edge detectors, coherent correlation detector (CCD), and pilot performance enhancement compared with different Rayleigh and Ricean fading conditions. For MB-OFDM UWB, we have considered the performance analysis and design criteria of MIMO OFDM UWB systems for high data-rate wireless transmission. The signal processing of frame detection, time synchronization, frequency synchronization, channel estimation, synchronization tracking using pilot subcarriers and 2-D MIMO detection algorithms is discussed. The main contributions of this thesis are 1) Compared with receiver employing the Maximum Likelihood (ML) detector, the receiver employing a low-complexity linear Minimum Mean Square Error (MMSE) demodulator can perform a limited performance loss in spatially uncorrelated channels. 2) Due to the operations of spreading and dispreading combined with MMSE-based frequency domain equalization, the adverse effects of the low-SNR subcarriers on the average BER performance are potentially improved. This is a direct consequence of spreading, because even if the signal corresponding to a specific chip is obliterated by a deep frequency domain channel fade, after dispreading these effects are spread over the Walsh-Hadamard Transform (WHT) length. Hence there is a high chance of still recovering all the partially affected subcarrier symbols without errors. 3) As a test case, the OFDM-based 200 Mb/s MB-UWB network (IEEE 802.15.3a) are considered, but the simulation results are shown above section. 4) The simulation channel can be easily applied to NOS and NLOS situation with WHT transformation from Simulink model. The fading channels of UWB with low/high signal to noise ratio (SNR), multipath effects, and multiuser interference may introduce large errors in location. The location engine performs the following tasks: █ Data fusion techniques: Different data fusion techniques are implemented using Time Of Arrival (TOA), Angle Of Arrival (AOA), or a combination of both. █ Channel modeling: A multipath, multiuser channel environment is created that models path loss, shadowing, Rayleigh fading, and Doppler frequency effects. █ Parameter estimation: TOA and AOA estimation algorithms are implemented as part of the location finding engine. Different variations of algorithms are implemented for performance and comparison purposes. █ Configuring the physical layer for different wireless networks. Among the programmable parameters are spreading factor, packet size, training length, constellation type, modulation technique, carrier frequency, level of transmitted signal power, and the number of antennas at the transmitter for both sides. █ Configuring the mobile user conditions. The wireless channel models depend on the Doppler frequencies present in the environment. The Doppler frequency depends on the mobile speed and carrier frequencies as input and generates a Rayleigh fading channel with the U-shape Doppler spectrum. █ Configuring the environmental parameters and network geographical structure. One of the factors affecting the performance of the wireless location system is the environment type (e.g., bad urban, urban, suburban, or rural area). For example, in a bad urban area with many blocking objects and buildings, non-line of sight (NLOS) effects plays an important role in the estimation accuracy.

In order to solve the highly nonlinear nonconvex problem of the mathematical model in Bluetooth (angle of arrival (AOA)) localization, this paper proposes an alternating minimization algorithm for three‐dimensional AOA localization. The algorithm method converts the solving error minimization solving three‐dimensional coordinate problem in Bluetooth three‐dimensional AOA localization model to solving four single‐variable problems and alternately optimizes each variable to get the global optimal solution, which reduces the complexity of the model solving and achieves the high‐precision localization performance without auxiliary information. The test is carried out in two states: dynamic line of sight (LOS) and static non–line of sight (NLOS), and the results show that the proposed method meets the demand for indoor positioning accuracy with a positioning error of 16 cm in LOS environment and 34 cm in NLOS environment.

Recently, there has been much interest in accurate determination of mobile user locations in cellular environments. A general approach to this geolocation problem is to gather time-of-arrival measurements from a number of base stations (BSs) and to estimate user locations using the traditional least square approach. However, in nonline-of-sight (NLOS) situations, measurements are significantly biased. Hence, very large errors in location estimation may be introduced when traditional techniques are adopted. For this reason, before employing an algorithm for location estimation, it is useful to know which BS's are in line-of-sight (LOS) and which are in NLOS of the mobile station. In this paper, a nonparametric approach to this NLOS identification problem is proposed. Since the statistics of NLOS errors are usually unknown, a nonparametric probability density estimation technique is employed to approximate the distribution of the measurements. Then, an appropriate metric is used to determine the distance between the distribution of the measurements and the distribution of the measurement noise. Depending on the closeness of the distributions, the propagation environment is classified as LOS or NLOS. In a situation where reliability of measurements from a BS is to be quantified, the distance can be used to represent the reliability of the measurements as well as to classify the station.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

Relative positioning accuracy between two devices is dependent on the precise range measurements. Ultra-wideband (UWB) technology is one of the popular and widely used technologies to achieve centimeter-level accuracy in range measurement. Nevertheless, harsh indoor environments, multipath issues, reflections, and bias due to antenna delay degrade the range measurement performance in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This article proposes an efficient and robust method to mitigate range measurement error in LOS and NLOS conditions by combining the latest artificial intelligence technology. A GP-enhanced non-linear function is proposed to mitigate the range bias in LOS scenarios. Moreover, NLOS identification based on the sliding window and Bayesian Conv-BLSTM method is utilized to mitigate range error due to the non-line-of-sight conditions. A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. Furthermore, moving average and min-max removing methods are utilized to minimize the standard deviation in the range measurements in both scenarios. Extensive experimentation with different settings and configurations has proven the effectiveness of our methodology and demonstrated the feasibility of our robust UWB range error mitigation for LOS and NLOS scenarios.

In Wireless communications, one of the main problems of uncooperative emitters localization based on Received Signal Strength (RSS) measurements is the Non-Line-of-Sight (NLOS) propagation. The weighting approach is among the important NLOS mitigation approaches whereby the system attempts to localize with both Line-of-Sight (LOS) and NLOS measurements. In the frame of uncooperative emitters and in intra cell environnement, an Approximate Maximum Likelihood (AML) based solution is proposed. This solution uses the relative RSSs observed at different locations. The proposed inta cell localization algorithm consists of a selective and hybrid AML estimator which combines the weighted ranges to the best three nodes with the angular beam seen at the serving BS. Numerical results prove that the proposed algorithm gives better performance compared to previous approaches.

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

Non-line-of-sight (NLOS) propagation degrades the performance of wireless location systems. Thus, developing algorithms that are robust to NLOS is of great importance. This paper introduces a new location technique that utilizes time of arrival (TOA) and angle of arrival (AOA) measurements. In the proposed method, we assume the signal from the mobile station reaches each base station via one dominant scatterer. By including the scatterer's coordinates as unknowns in a TOA/AOA-based cost function and imposing some equality and inequality constrains, the location of the mobile station (MS) is shown to significantly improve. The performance of the proposed algorithm is assessed and compared with that of existing algorithms through extensive simulations.

In order to evaluate spatial processing techniques such as MIMO for wideband mobile communications, a Time-Spatial Propagation (TSP) model is required. The Time-Spatial profile consists of the delay profile, the spatial arrival angular profiles at the base station (BS) and the mobile station (MS) for traveling waves. One key parameter of the TSP model is carrier frequency. We have proposed a Time- Spatial profile prediction formula for wideband mobile communication systems in Non-line of sight (NLOS) environments and clarified that the Time- Spatial profile does not depend on the carrier frequency. We have also carried out measurements to develop the Time-Spatial profile prediction formula for Line of sight (LOS) environments. In this paper, we evaluate the carrier frequency characteristics of the Time-Spatial profile in Line of sight (LOS) environments based on the measurement data, and show that our proposed Time-Spatial profile prediction formula in LOS environments is valid for various carrier frequencies.

In this paper we proposed a location and tracking algorithm with non-line of sight (NLOS) errors for mobile station (MS). The algorithm is able to correct the NLOS errors using the Back Propagation (BP) neural network on TDOA measurements, and the positions of MS can be estimated by the Chan's algorithm. Furthermore, cooperating the correlation detection gate, the MS is tracked by the algorithm. Simulations results show that the proposed algorithm could be efficiently locate the MS not only in the static state but also in the dynamic state.