RSS Localization Algorithm Based on Nonline of Sight Identification for Wireless Sensor Network

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.

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.

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

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.

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.

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.

This paper proposes a method for mitigation of non-line-of-sight (NLOS) situations in a real time location system (RTLS) using an impulse radio ultra wide band (IR-UWB). Based on our experiment, we found that there is a slight chance to meet the line-of-sight (LOS) at all the readers of RTLS in a realistic scenario. In the NLOS environment, there are two problems of packet-miss error and multi-path error as well. To compensate for the packet-miss error, this paper presents a new algorithm (PPTi), which uses previous time information before the time difference of arrival (TDOA) positioning technique calculates the coordinate of the target. We also employed the Kalman Filter (KF) to mitigate multi-path error. Our proposed algorithm provides a 5.9% increased positioning success rate and a 25.6% decreased root mean square error (RMSE) in an NLOS environment.

Sub-connected structure hybrid beamforming (SCS-HBF) exploiting both successful interference cancellation and zero-forcing (ZF) is tortured by frail performance when deployed for millimeter-Wave non-orthogonal multiple access (NOMA) system in a complete non-line of sight (NLOS) environment. Therefore, this letter articulates a sum-rate maximization for SCS-HBF-NOMA system built based on unique phased-ZF and dynamic power optimization schemes robust for both line of sight and NLOS environments. Radio frequency chain's diversity impact on the proposed scheme's performance is also analyzed.

Based on the positive bias property of the time of arrival(TOA) measurement error caused by the non-line-of-sight(NLOS) propagation, a simple and effective three dimensional(3D) geometrical localization algorithm was proposed, the algorithm needs no prior knowledge of time delay distribution of TOA, and only linear regression was used to estimate the parameters of the relationship between the NLOS distance error and the true distance, thus, the approximate real distance between mobile terminal (MT) and base station (BS) was reduced, then, the 3D geometric localization of mobile terminal was carried out by the least square method. The experimental results shows the effectiveness of the algorithm, and the positional accuracy is far higher than the required accuracy by E-911 in NLOS environments.

The present paper considers how to improve estimation accuracy in a time of arrival (TOA) localization system using UWB in a non-line-of-sight (NLOS) environment for a sensor network. This algorithm consists of step-by-step compensation on the basis of two approaches considering a reference position which is estimated from data affected by NLOS delay. The first consideration consists of determining NLOS delays for each node, performing compensation to alleviate the effect on line of sight (LOS) nodes through a step-by-step compensation for the NLOS delay. The second consideration consists of compensating the effect of NLOS delay on the position determined on the basis of node distribution and geometrical relations of the estimated positions. Using these considerations, we show that the proposed algorithm outperforms the conventional algorithm in terms of estimation accuracy.

Due to the wide deployment of wireless local area networks (WLANs) and the easy acquirement of received signal strength (RSS), indoor localization based on RSS has attracted considerable attention in both academia and industry. In this paper, we extract the scatter factor from RSS and then propose a novel indoor localization scheme based on location fingerprinting and curve fitting techniques. The scheme is set up in two phases. In the offline phase, we create a fingerprint and apply the curve fitting technique to construct fitted RSS-distance functions in LOS (Line Of Sight) and NLOS (None Line Of Sight) condition, respectively. In online positioning phase, the proposed optimized fingerprinting-based localization algorithm (OFPL) uses trilateration technique to assist the fingerprinting localization in choosing the proper location of the receiver. Through conducting field and extensive experiments, we can draw the conclusion that our proposed algorithm can obtain some improvement in localization accuracy compared with the classical fingerprinting-based localization.

Wireless localization has become an important issue in internet of things, but the non-line-of-sight (NLOS) propagation will degrade the performance of traditional localization algorithms. Therefore, an optimization-based localization algorithm is put forward by exploiting the linear line of position (LLOP) scheme, where the proposed algorithm models the NLOS problem as a quadratic programming problem, and then the position of the mobile node is estimated by solving this problem. Simulations show that the proposed algorithm can mitigate the NLOS error significantly if there are many base stations or there exist a few LOS base stations. Moreover, the simulation also proves that the proposed algorithm outperforms other algorithms in the NLOS environment.

Indoor localization system based on received signal strength (RSS) often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors. To identify non-line-of-sight status and line-of-sight (LOS) status and improve the accuracy of indoor localization, a D-S evidence theory based NLOS identification algorithm was proposed. The algorithm is divided into two parts. In the first part, respectively extract a variety of RSS features in NLOS and LOS status and choose the appropriate filter for data processing; in the second part, RSS features fused by D-S evidence theory are used to identify NLOS and LOS status. In an actual test environment, the algorithm was applied to fingerprinting localization, then analyzed the advantages of fusing RSS features and the influence of RSS features selection on experimental results. Finally, compared with several commonly used NLOS and LOS identification algorithm, it shows the proposed algorithm can deal with the indoor localization environment with obstacles with a high localization accuracy and good stability.

In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in this paper, which can reduce the impact on the positioning accuracy of NLOS effectively on the basis of the measurement error correction. RBF neural network has a faster learning characteristic and the ability of approximate arbitrary nonlinear mapping. In the process of studying, RBF neural network adjusts to the quantity of the nodes according to corresponding additive strategy and removing strategy. The newly-formed network has a simple structure with high accuracy and better adaptive ability. After correcting the error, reuse Taylor series expansion location algorithm for positioning. The simulation results indicate that the proposed algorithm has high location accuracy, the performance is better than RBF-Taylor algorithm, LS-Taylor algorithm, Chan algorithm and LS algorithm in NLOS environment.