scholarly journals A Beacon-based Approach for RF Source localization in Outdoor NLOS Environment for Search and Rescue Missions

2021 ◽  
Vol 18 (23) ◽  
pp. 685
Author(s):  
Muhammad Hassan Fares ◽  
Hadi Moradi ◽  
Mahmoud Shahabadi ◽  
Yasser Mohanna
Keyword(s):  
Source Localization ◽  
Rural Areas ◽  
Search Time ◽  
Mobile Network ◽  
Search Space ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Performance Metric ◽  
Rf Signal ◽  
Non Line Of Sight

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

scholarly journals Non-Line-of-Sight Error Mitigation in Wireless Communication Systems

2011 ◽  
Vol 1 ◽  
pp. 173-177
Author(s):  
Szu Lin Su ◽  
Yi Wen Su ◽  
Ho Nien Shou ◽  
Chien Sheng Chen
Keyword(s):  
Communication Systems ◽  
Hybrid Methods ◽  
Mobile Station ◽  
Base Stations ◽  
Superior Performance ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Error Statistics ◽  
Non Line Of Sight

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).

CẢI THIỆN ĐỘ CHÍNH XÁC TRONG ĐỊNH VỊ TRONG NHÀ DÙNG GIẢI PHÁP KẾT HỢP AOA VÀ BỘ LỌC KALMAN CHO HỆ THỐNG MASSIVE MIMO

2021 ◽  
pp. 57-62
Author(s):  
Hằng
Keyword(s):  
Massive Mimo ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Non Line Of Sight

Trong bài báo này, giải pháp hiệu quả cải thiện độ chính xác của định vị trong nhà, dựa trên góc tới AOA( Angle Of Arrival) kết hợp với bộ lọc Kalman đã được đề xuất. Giải pháp này có thể cải thiện độ chính xác cho bài toán định vị nguồn phát trong môi trường trong nhà so với phương pháp AOA truyền thống. Hai kịch bản được tạo ra để kiểm tra hiệu suất của giải pháp đề xuất. Kịch bản thứ nhất môi trường truyền dẫn tồn tại đường LOS ( Line Of Sight) và NLOS, kịch bản thứ hai môi trường truyền dẫn chỉ tồn tại các đường NLOS(Non - Line Of Sight) do các đường LOS bị suy giảm. Kết quả mô phỏng cho thấy, giải pháp đề xuất đạt được độ chính xác cao hơn so với phương pháp AOA truyền thống. Đặc biệt, khi sai số định vị dưới 2m và môi trường chỉ có NLOS, thuật toán đề xuất đạt độ chính xác cao hơn 20% so với thuật toán AOA truyền thống.

scholarly journals Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shixun Wu ◽  
Shengjun Zhang ◽  
Kai Xu ◽  
Darong Huang
Keyword(s):  
Base Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Probability Weighting ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Chi Square ◽  
Home Base ◽  
Angle Measurements ◽  
Non Line Of Sight

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

scholarly journals A time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm for indoor non-line-of-sight locationing

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986035 ◽  
Author(s):  
Chong Shen ◽  
Chengxiao Wang ◽  
Kun Zhang ◽  
Xianpeng Wang ◽  
Jing Liu
Keyword(s):  
Steepest Descent ◽  
Time Difference ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Fusion Algorithm ◽  
Time Difference Of Arrival ◽  
Positioning Accuracy ◽  
Descent Algorithm ◽  
Steepest Descent Algorithm ◽  
Non Line Of Sight

In complex indoor propagation environment, the non-line-of-sight error caused by various obstacles brings great error to node positioning. Choosing the appropriate signal transmission methods is important to improve node indoor positioning accuracy. In this research, ultra-wideband technology, as baseband with high theoretical positioning accuracy and real-time performance, is implemented to transmit indoor signals. The proposed fusion algorithm with ultra-wideband baseband takes advantages from both time difference of arrival and angle of arrival algorithms, combined through the steepest descent algorithm. The non-line-of-sight signal estimation error is iteratively eliminated to achieve effective positioning accuracy. The experimental results indicate that the novel time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm can largely improve node positioning accuracy and stability.

IMU-Aided Dual-Frequency RFID Based Localization in LOS/NLOS Hybrid Environment

2017 ◽  
Author(s):  
Jie Wu ◽  
MingHua Zhu ◽  
Bo Xiao ◽  
Wei He
Keyword(s):  
Indoor Localization ◽  
Measurement Unit ◽  
Line Of Sight ◽  
Dual Frequency ◽  
Site Survey ◽  
Line Of Sight Propagation ◽  
The Mean ◽  
Position Precision ◽  
Rf Signal ◽  
Non Line Of Sight

The mitigation of NLOS (non-line-of-sight) propagation conditions is one of main challenges in wireless signals based indoor localization. When RFID localization technology is applied in applications, RSS fluctuates frequently due to the shade and multipath effect of RF signal, which could result in localization inaccuracy. In particularly, when tags carriers are walking in LOS (line-of-sight) and NLOS hybrid environment, great attenuation of RSS will happen, which would result in great location deviation. The paper proposes an IMU-assisted (Inertial Measurement Unit) RFID based indoor localization in LOS/NLOS hybrid environment. The proposed method includes three improvements over previous RSS based positioning methods: IMU aided RSS filtering, IMU aided LOS/NLOS distinguishing and IMU aided LOS/NLOS environment switching. Also, CRLB (Cramér-Rao Low Bound) is calculated to prove theoretically that indoor positioning accuracy for proposed method in LOS/NLOS mixed environment is higher than position precision of only use RSS information. Simulation and experiments are conducted to show that proposed method can reduce the mean positioning error to around 3 meters without site survey.

scholarly journals Estimating Directional Data From Network Topology for Improving Tracking Performance

2019 ◽  
Vol 8 (2) ◽  
pp. 30 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko ◽  
Rui Dinis ◽  
Paulo Montezuma
Keyword(s):  
Prior Knowledge ◽  
Polar Space ◽  
Reference Points ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Measurement Models ◽  
Novel Approach ◽  
Negative Effect ◽  
Non Line Of Sight

This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.

Non-line-of-sight sound source localization using matched-field processing

2012 ◽  
Vol 131 (1) ◽  
pp. 292-302 ◽  
Author(s):  
Victor Singh ◽  
Katherine E. Knisely ◽  
Serdar H. Yönak ◽  
Karl Grosh ◽  
David R. Dowling
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization ◽  
Line Of Sight ◽  
Matched Field Processing ◽  
Non Line Of Sight

scholarly journals Combination linear lines of position and neural network for mobile station location estimation

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771738 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Mobile Station ◽  
Base Station ◽  
Location Estimation ◽  
Base Stations ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Marquardt Algorithm ◽  
Station Location ◽  
Non Line Of Sight

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

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