Mobile Position Estimation Using Received Signal Strength and Time of Arrival in Mixed LOS/NLOS Environments
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Time-of-arrival (TOA) estimation and the inherent accuracy of the mobile position estimation have been considered for the wireless non-line-of-sight (NLOS) geolocation. To increase the TOA estimation accuracy, it is often useful to exploit additional information, such as path attenuation or path loss, which can in principle be observed from the received signal strength (RSS). This chapter investigates the TOA estimation techniques for the hybrid RSS-TOA localization in mixed line-of-sight (LOS)/NLOS environments. It explains how to choose a sufficient number of the TOAs so that the computation burden of the mobile position estimation can be reduced without any performance degradation. More importantly, the chapter shows how to determine a mobile position from such sufficient TOAs while the error performance attains certain performance bounds. The results shared in the chapter help a system designer to select the number of nodes that should be used in the process of localization and NLOS mitigation. Controlled Vocabulary Terms position measurement; time of arrival estimationKeywords:
Non-line-of-sight propagation
Geolocation
RSS
Time of arrival
Position (finance)
Angle of arrival
Ranging
Non-line-of-sight propagation
RSS
Geolocation
Time of arrival
Cramér–Rao bound
Angle of arrival
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The location of people, mobile terminals and equipment is highly desirable for operational and safety enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflections, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh mining environment, unreliable measurements of location metrics such as RSS, AOA and TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the Wide Band Neural Network-Locate (WBNN-Locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses data for LOS and NLOS situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).
Non-line-of-sight propagation
Geolocation
Time of arrival
RSS
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Geolocation
Signal strength
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The location of mobile station is an important issue for wireless communication systems. A location estimation scheme using fuzzy-based Interacting Multiple Model (IMM) smoother is proposed in this paper. It combines the time-of-arrival (TOA) and the received signal strength (RSS) measurements to achieve high location accuracy. The fuzzy technique is used to interpolate several linear equations to approximate the nonlinear RSS measurement. The IMM is employed as a switch between the line-of-sight (LOS) and non-line-of-sight (NLOS) states which are considered to be a Markov process with two interactive modes. By integrating the fuzzy filtering and the IMM method for range estimation between the corresponding base station (BS) and mobile station (MS), the proposed robust scheme, in association with data fusion, can efficiently mitigate the NLOS effects on the measurement range error. Simulation results are given to confirm the performance of the proposed method.
Non-line-of-sight propagation
RSS
Sensor Fusion
Time of arrival
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Citations (58)
Analysis and estimation of the time of arrival and received signal strength for indoor geolocation using MATLAB describes an indoor geolocation localization which either use the received signal strength (RSS) or time of arrival (TOA) of the received signal as their localization metric. Though time of arrival based systems are sensitive to the available bandwidth and also to the occurrence of undetected direct path (UDP) channel conditions which RSS based system are less sensitive to the bandwidth as more resilient to undetected conditions. This paper demonstrate the availability of radio channel modeling techniques to eliminate the costly finger printing process in pattern recognition algorithms by introducing ray tracing (RT) assisted by RSS and TOA based algorithms. The results in figure 8 which shows the effect of pathloss on signal reception, showing free path loss reduces when plotted with rhe height of the building which can be used for achieving localization. it was also disovered that path loss also contributes to signal delay, the plot in figure 12 which is a probability distribution of received signal strength at different location which detect signal at the point where maximum signal was received , this RSS at fixed positions can be used to determine geolocation.
Geolocation
RSS
Time of arrival
SIGNAL (programming language)
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The location of people, mobile terminals and equipment is highly desirable for operational enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflection, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest direct path between transmitter and receiver are the main sources of range measurement errors. Unreliable measurements of location metrics such as received signal strengths (RSS), angles of arrival (AOA) and times of arrival (TOA) or time differences of arrival (TDOA), result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the wide band neural network-locate (WBNN-locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface).
Non-line-of-sight propagation
Geolocation
Time of arrival
Angle of arrival
RSS
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Citations (185)
The indoor localization has received considerable attention in the field of positioning. It has been reported that time-of-arrival (ToA) based localization performs superior in comparison to the received-signal-strength (RSS) and the angle-of-arrival (AoA) based techniques in line-of-sight (LOS) condition. However, the accuracy of such systems is limited mainly due to unexpected large ranging errors observed in indoor environment, which are primarily caused by obstruction of the direct path and the effect of diffraction of the radio waves around the edges of micro-metallic objects. It is known that the analysis of effects of the micro-metallic objects on the accuracy of the range estimates could be indeed a challenging problem. In this paper, we investigate both simulation and analytical approaches based on applicability of electromagnetic (EM) methods to analyze the effects of micro-metallic objects on the accuracy of the range estimates. According to the first approach, the results of MATLAB based 2D finite-difference-time-domain (FDTD) simulation are compared to the 500 MHz bandwidth channel profiles obtained from a real-time frequency-domain measurement to analyze the accuracy of 2D simulation. Subsequently, we compare the achieved results of our analytical calculations to that of 3GHz channel profile measurements. Our studies reveal the fact that the presented simulation and analytical results are in close agreement with the results attained from the measurement campaign.
Non-line-of-sight propagation
RSS
Time of arrival
Ranging
Geolocation
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Citations (3)
The location of a mobile robot is highly desirable for operational enhancements in indoor environments. In an in-building environment, the multipath caused by reflection and diffraction, and the obstruction and/or the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh indoor environment, unreliable measurements of location metrics such as received signal strength (RSS), angle of arrival (AOA) and time or time difference of arrival TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a method for mobile robot location using WLAN's received power (RSS) data applied to an artificial neural network (ANN). The proposed system learns off-line the location RSS 'signatures' for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile robot against the learned set of 'signatures' to accurately locate its position. The location precision of the proposed system, applied in an in-building environment, has been found to be 0.5 meter for 90% of trained data and about 5 meters for 58% of untrained data.
Non-line-of-sight propagation
Geolocation
RSS
Angle of arrival
Time of arrival
Cite
Citations (8)
Time-of-arrival (TOA) estimation and the inherent accuracy of the mobile position estimation have been considered for the wireless non-line-of-sight (NLOS) geolocation. To increase the TOA estimation accuracy, it is often useful to exploit additional information, such as path attenuation or path loss, which can in principle be observed from the received signal strength (RSS). This chapter investigates the TOA estimation techniques for the hybrid RSS-TOA localization in mixed line-of-sight (LOS)/NLOS environments. It explains how to choose a sufficient number of the TOAs so that the computation burden of the mobile position estimation can be reduced without any performance degradation. More importantly, the chapter shows how to determine a mobile position from such sufficient TOAs while the error performance attains certain performance bounds. The results shared in the chapter help a system designer to select the number of nodes that should be used in the process of localization and NLOS mitigation. Controlled Vocabulary Terms position measurement; time of arrival estimation
Non-line-of-sight propagation
Geolocation
RSS
Time of arrival
Position (finance)
Angle of arrival
Cite
Citations (0)