An Error Probability Location Estimation Algorithm for Use in Substations

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.

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.

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.

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.

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.

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.

Wireless Sensor Network is a wireless network technology that includes sensor nodes and embedded systems. WSN has several advantages: it is cheaper for large-scale applications, can withstand extreme environments, and data transmission is relatively more stable. One of the WSN devices is nRF24L01+. Within the specifications given, the maximum communication distance is 1.1 km. However, the most effective distance for transmitting data in line of sight and non-line of sight is still unknown. Therefore, testing and analysis are needed so that the nRF24L01+ device can be used optimally for communication and data transmission. Through testing analysis on nRF24L01+ line of sight, Kuta beach location in Bali and non-line of sight on the STMIK STIKOM Indonesia campus. The effective communication distance of the nRF24L01+ module in line of sight is between 1 and 1000 meters. The distance of 1000 meters is the limit of the effective distance for sending data, and the packet loss rate is less than 15% which is included in the medium category. Meanwhile, in the non-line of sight, the effective distance of the nRF24L01+ communication module is 20 meters, and the packet loss is close to 15%, which is a moderate level limit. With the analysis module, nRF24L01+ can be a reference in determining the effective distance on WSN nRF24L01+ in determining remote control equipment data communication.

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.

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.

In future 5G systems, the millimeter wave (mmWave) band will be used to support a large capacity for current mobile broadband. Therefore, the radio access technology (RAT) should be made available for 5G devices to help in distinct situations, for example device-to-device communications (D2D) and multi-hops. This paper presents ultra-wideband channel measurements for millimeter wave bands at 19, 28, and 38 GHz. We used an ultra-wideband channel sounder (1 GHz bandwidth) in an indoor to outdoor (I2O) environment for non-line-of-sight (NLOS) scenarios. In an NLOS environment, there is no direct path (line of sight), and all of the contributed paths are received from different physical objects by refection propagation phenomena. Hence, in this work, a directional horn antenna (high gain) was used at the transmitter, while an omnidirectional antenna was used at the receiver to collect the radio signals from all directions. The path loss and temporal dispersion were examined based on the acquired measurement data—the 5G propagation characteristics. Two different path loss models were used, namely close-in (CI) free space reference distance and alpha-beta-gamma (ABG) models. The time dispersion parameters were provided based on a mean excess delay, a root mean square (RMS) delay spread, and a maximum excess delay. The path loss exponent for this NLOS specific environment was found to be low for all of the proposed frequencies, and the RMS delay spread values were less than 30 ns for all of the measured frequencies, and the average RMS delay spread values were 19.2, 19.3, and 20.3 ns for 19, 28, and 38 GHz frequencies, respectively. Moreover, the mean excess delay values were found also at 26.1, 25.8, and 27.3 ns for 19, 28, and 38 GHz frequencies, respectively. The propagation signal through the NLOS channel at 19, 28, and 38 GHz was strong with a low delay; it is concluded that these bands are reliable for 5G systems in short-range applications.

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.

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

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.

Positioning is one of the key technologies to support the application of intelligent connected vehicles (ICV). GNSS, as one of main measures of ICV positioning, cannot be available in indoor scenarios. The emergence of indoor wireless positioning technologies, such as UWB and WIFI, has solved this problem. However, they are affected by the non line of sight (NLoS) propagation of the signal, resulting in lower positioning accuracy. To improve the vehicle indoor positioning performance with NLoS state, we propose an ICV indoor positioning method called CGPC-EMPF. First, the Chan-GN algorithm is improved with permutation and combination consistency, whose result is used as the observation of particle filter. For the vehicle motion prediction without a Strapdown inertial navigation system, we propose a vehicle motion prediction model based on a priori information. Then, the NLoS correction model is established by a particle filter. Finally, the corrected measurements are substituted into the Chan-GN algorithm to obtain the final results. It realizes accuracy enhancement by introducing a vehicle motion state to correct errors coursed by NLoS. Compared with the multi-sensor fusion positioning algorithm, our method has lower complexity and cost due to a single sensor application.