Bluetooth Three‐Dimensional AOA Positioning Method Based on the AMA‐SDM

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.

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.

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions.
 HIGHLIGHTS
 
 A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition
 A reflector’s pose is determined based on the signal received from beacons
 The reflector’s pose is used to determine the location of the RF source
 One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low
 
 GRAPHICAL ABSTRACT

With the development of Internet of Thing (IoT), positioning is of a significant importance in indoor environments. Thus, recent years, localization technology has advanced greatly aimed at localizing the target by utilizing existing infrastructure such as WiFi. However, in a complex indoor environment, the performance of localization system tend to fluctuates when there is Non-Line-of-Sight (NLoS) propagation. Specially, for the high accuracy localization system using Angle of Arrival (AoA) or Time of Flight (ToF) of the direct signal path, the robustness is a challenge since the propagations of signal are not always in Line-of-Sight (LoS) due to the complicated indoor environment. Environment sensing is of significant importance for improving the accuracy and robustness of high accuracy localization system working in NLoS environment. In this paper, we propose a context-aware algorithm by using the spatial properties of multipath signal to aid indoor localization. Firstly, we investigate the influence of Signal-Noise-Ratio (SNR) on the distribution of signal spatial parameters, i.e., AoA and ToF, by deriving a mathematical model. Then, we creatively employ the method of clustering tendency analysis to construct a LoS/NLoS identification collaborating with non-parametric statistical method. Finally, we deploy a localization system with the proposed context-aware algorithm. The extensive experimental result shows that the proposed algorithm can sense environment well and further improve the accuracy and robustness of localization system.

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).

Angle of arrival (AOA) and received signal strength (RSS) measurements have been commonly used in wireless localization due to easy access and simple implementation. In this paper, we investigate source localization using the AOA-only and hybrid RSS/AOA measurements, respectively. In AOA localization, we approximate the angle error using a range-related quantity. Then the optimization problem based on maximum likelihood (ML) is converted to a convex semidefinite programming (SDP) problem. In hybrid AOA/RSS localization, the ML estimator is decomposed into an RSS part and an AOA part. The AOA part follows a similar procedure as in the AOA localization. Taylor series expansion and relaxation are applied in optimizing the RSS part. These two parts are closely related through the range. The proposed methods avoid the nonconvexity in the original ML estimators for both AOA-only and hybrid AOA/RSS localization problems. Numerical examples show good performance of the proposed methods in both AOA and hybrid AOA/RSS localizations. They are close to or better than the LS methods in the literature.

Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently direction-finding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

With the rise of the Internet of Things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. Therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. The main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. The simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm.

The 38GHz millimeter-wave propagation characteristics are simulated and analyzed using the method of shooting and bouncing ray tracing/image (SBR/IM) in the indoor environment. The propagation characteristics in the line-of-sight(LOS) and non-line-of-sight (NLOS) environment are discussed. The effects of the path loss and root mean square delay spread are analyzed. Results of this paper can be capable of guiding parameter selection when realizing the scheme in the 5G system.

UWB signal reception can be accomplished by coherent or non-coherent receivers. Correlation receiver is a type of coherent receiver while energy detection (ED) receiver belongs to non-coherent type. In this paper, we evaluate their ranging performance using the channel profiles measured in a typical indoor office environment. The effects of ranging parameters such as search window length are also investigated. The results show that correlation receiver outperforms ED receiver in the line-of-sight (LOS) scenario and smaller integration interval gives better performance for ED. For the non-line-of-sight (NLOS) scenario, surprisingly, ED receiver outperforms correlation receiver and larger integration interval brings better performance for ED when the detection threshold exceeds certain level. The study also shows that regardless of the receiver types used, the resultant ranging errors for the LOS or NLOS environments can be described by unified models with the parameters being distance dependent.

Precise positioning of agent is a challenging task due to the non line-of-sight (NLoS) propagation in ad-hoc location-aware networks. In an effort to enhance location accuracy, we address a novel scheme to alleviate NLoS error based on the appealing feature of linear lines-of-position (LLoP) and taylor series-least square (TS-LS). This approach utilizes single measurements at a set of anchors and comprises three stages: combination-refine stage, line-of-sight (LoS) reconstruction and taylor-optimization. The second stage reconstructs the true ranges by subtracting the NLoS measurements from the maximum measurement residual. Simulation results and discussions are given to evaluate the performance in typical NLoS environments. The conclusions infer that the proposed algorithm outperforms the traditional algorithms in dense NLoS environment at a reasonable computational complexity.

An analytic approach is presented to evaluate the Bit Error Rate (BER) and read range of a UWB chipless RFID network using diversity technique at the reader receiver for Rayleigh fading over the channels. The analysis is carried out with multiresonator based chipless RFID tags for a UWB system of frequency range from 3GHz – 6GHz and bandwidth of 500MHz in an outdoor and farm non line of sight (NLOS) environment. SISO configuration is used to communicate from reader to tag and SIMO configuration is used to communicate from tag to reader. Maximal ratio combining technique is used in the reader for diversity reception of the tag. BPSK, QPSK and M-ary PSK are considered with coherent demodulation to evaluate the BER performance and a read range of 548.00m at the BER of 10−06 could be achieved. The analyzed approach not only gives an improved result compared to the previous research works but also gives a clear insight regarding the interrelationship between BER, detection ranges, reader received power, number of receiving antenna and read range improvement.

By acquiring the location information of the mobile target using the location estimation algorithm based on the wireless sensor network, human life in the substation and the safe operation of the mobile device can be protected, and the intelligent management of the mobile target in the substation can be realized. This is of great significance for realizing the intelligence and informationization of substation management. The environment in the substation is complex and there is Non Line of Sight (NLOS) propagation. In view of the large error in the results of the existing mobile position estimation algorithm in the NLOS environment, this paper proposes an error probability localization algorithm suitable for substation. The simulation results show that the proposed algorithm has higher positioning accuracy than the existing algorithms.

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.