- Research Article
- Open Access
Accurate and Integrated Localization System for Indoor Environments Based on IEEE 802.11 Round-Trip Time Measurements
© Alfonso Bahillo et al. 2010
- Received: 20 July 2009
- Accepted: 17 February 2010
- Published: 6 May 2010
The presence of (Non line of Sight) NLOS propagation paths has been considered the main drawback for localization schemes to estimate the position of a (Mobile User) MU in an indoor environment. This paper presents a comprehensive wireless localization system based on (Round-Trip Time) RTT measurements in an unmodified IEEE 802.11 wireless network. It overcomes the NLOS impairment by implementing the (Prior NLOS Measurements Correction) PNMC technique. At first, the RTT measurements are performed with a novel electronic circuit avoiding the need for time synchronization between wireless nodes. At second, the distance between the MU and each reference device is estimated by using a simple linear regression function that best relates the RTT to the distance in (Line of Sight) LOS. Assuming that LOS in an indoor environment is a simplification of reality hence, the PNMC technique is applied to correct the NLOS effect. At third, assuming known the position of the reference devices, a multilateration technique is implemented to obtain the MU position. Finally, the localization system coupled with measurements demonstrates that the system outperforms the conventional time-based indoor localization schemes without using any tracking technique such as Kalman filters or Bayesian methods.
- Global Navigation Satellite System
- Global Navigation Satellite System
- Access Point
- Print Circuit Board
- Indoor Environment
There is a proliferating demand for both commercial and governmental applications of wireless localization services that ascertain the position of a (mobile user) MU in an indoor environment . Indoor location information can add many potential applications such as persons with special care tracking inmates monitoring, or helping policeman, fireman or soldiers to finish their missions inside buildings. However, currently signals coming from Global Navigation Satellite System (GNSS) cannot penetrate into indoor environments. Hence, alternative wireless infrastructures which offer indoor coverage have to be used. There are several existing wireless infrastructures deployed in indoor environments like ultrasonic, infrared, and artificial vision, that have been considered for indoor localization, but radiofrequency-based systems predominate today, due to their availability and low cost [2, 3]. Up to date, few wireless infrastructures that operate inside buildings are as extensively deployed and used as IEEE 802.11, a reason why this wireless technology is the best candidate for the development of an indoor localization system.
Localization methods are further classified by the measurable quantities obtained from the transmitted signals. Thus, it can be angle based as the angle of arrival (AOA) , range based as the measured time of arrival (TOA) [5–7] or the received signal strength (RSS) [8–10] of the MU's signal at the reference devices. This information, received on the MU, establishes a geometric relationship between the MU to be located and the reference devices. AOA measurements require antenna arrays and they are not available to inexpensive systems, while RSS measurements are widely available and provide cost-effective means of localization. However, in indoor environments the propagation phenomena cause the attenuation of the signal to poorly correlate with distance, resulting in inaccurate distance estimates. On the contrary, time-based methods are highly correlated with distance . But, as TOA measurements need for time synchronization between wireless nodes, round-trip time (RTT) measurements have been used in this paper. These measurements are obtained by using the electronic circuit proposed in . Once the RTT measurements have been performed between two wireless nodes, a model is used to relate the RTT to the actual distance that separates both nodes in LOSs. Previous essays  have used a similar electronic circuit to measure the RTT, but they use an empirical RTT-based model to estimate distances. Therefore, that model is not robust because it depends on the environment where the MU is going to be located.
Whichever the method used, a similar impairment is encountered related to indoor environments where the transmitted signal could only reach the receiver through reflected, diffracted, or scattered paths. Various NLOS (Non-Line-Of Sight) mitigation techniques have emerged to overcome this problem. They can be broadly classified in two groups, techniques which attempt to minimize the contribution of NLOS multipaths as  or techniques which focus on the identification of NLOS reference devices and discard them for localization . However, their reliability remains questionable in an indoor environment with abundant scatterers where almost all reference devices will be in NLOS. In this paper, the PNMC (Prior NLOS Measurements Correction) technique is used to correct the NLOS effect from distance estimates . This technique manages to introduce in the localization process the information that actually resides in the NLOS measurements. Once distances between the MU to be located and the reference devices in range are estimated, and assuming known the reference devices positions, different techniques could be applied to infer the MU position, like circular lateration  or hyperbolic lateration . In this paper, an MUltilateration technique that linearizes the problem of finding the MU position is proposed to reduce its complexity and to complete the wireless localization system.
The paper is divided as follows: Section 2 describes the driver responsible to automate the localization system and Section 3 describes the localization algorithm. Section 4 analyzes the accuracy of the localization system proposed in an indoor environment. Finally, Section 5 summarizes the main achievements.
1.1. Previous Work
The main mechanism that makes RTT measurements possible in an IEEE 802.11 wireless network with a minimum elapsed time in the access point (AP) is the RTS/CTS handshake mechanism [19, 20]. In this paper, the printed circuit board (PCB) proposed in  is used to measure the RTT of the RTS/CTS two-frame exchange mechanism between two IEEE 802.11 wireless nodes. The way in which the PCB works is as follows: the MU enables the measuring system before sending the RTS frame and disables it after receiving the CTS frame response. Within that time the PCB extracts both transmission pulses and receiver signals from the MU wireless adapter in such a way that the RTS frame departure is used as the trigger to start the count that would be stopped by the corresponding CTS frame arrival. Despite the short lapse of time between the measuring system activation and the RTS departure or between the RTS departure and the CTS arrival, a frame coming from other wireless nodes could interfere activating or deactivating the count, respectively. As this interference could occur, a filter which rejects measurements out of the expected range has been implemented. After the RTS/CTS handshake is completed the MU saves the state of the count.
The measuring system proposed has some limitations. Firstly, as the that governs the PCB is 44 MHz frequency, the 16-bit counter implemented on the PCB cannot measure RTTs over 1.489 ms, but this time is enough for wireless networks range. Secondly, as a frame coming from other wireless nodes could activate or deactivate the count within the short lapse of time in which the measuring system is enabled, a filter that rejects these undesirable measurements is implemented. Filter limits have been chosen based on previous trials where there were no other wireless nodes interfering. Finally, according to  the elapsed time in the AP, between receiving an RTS frame and sending the corresponding CTS frame, can be assumed to be constant when there are no other processes competing for the AP resources. Obviously, although the RTS frame has the highest priority in , it could be concurrent RTS frames coming from other MUs at the same AP increasing the load of the AP. In that case, if there are not enough APs in range to apply the localization algorithm, the wireless localization system delay increases, but the accuracy is not degraded thanks to the previous filter that rejects the RTT measurements that are out of the expected range.
In this paper, it is assumed that the PCB and the localization algorithm are performed in the MU side, although other combinations could be possible. The way in which the localization system obtains the MU position is by the localization algorithm that will be described in Section 3. The only input arguments needed by the localization algorithm are as follows: the APs positions that play the role of reference devices and whose positions are assumed to be known, and the RTT measurements that characterize the distance between the MU and each AP in range. Following the flowchart shown in Figure 1, the RTT measurements are obtained automatically by the PCB described in  thanks to the driver that works as follows.
( ) The MU wireless adapter scans the environment looking for beacon probes coming from the APs, which are commonly sent by each AP every 100 ms . The power level, based on the RSS indicator (RSSI), with its corresponding MAC source address are stored when the beacon reaches the MU wireless adapter. If the MU receives beacon probes coming from no more than two APs, the MU continues scanning the environment. Once more than two APs are in range, the following action is repeated for each AP.
( ) The MU wireless adapter communicates to the AP through the MAC address sending to it RTS frames. After receiving the corresponding CTS response frames, the corresponding RTTs are stored from each RTS/CTS frame exchange. Next, regarding the RTT estimator to be used, the Fisher information is computed from the RTT measurements to calculate the minimum number of RTS/CTS frames exchange (parameter in Figure 1) that are needed for a given confidence level and sample error. By this way, the localization system minimizes the number of RTS/CTS frames exchange and hence the use of the IEEE 802.11 channel which could slow down the packet delivery on the wireless network. In Appendix B, a detailed information is given about computing. Therefore,
(i)if the number of RTT measurements is lower than the computed parameter , the MU adapter sends another RTS frames,
(ii)if not, the distance between the MU and the corresponding AP is characterized by the RTT measurements carried out until that moment,
( ) once this process has been repeated for each AP in range, and if the number of APs in range is no less than three, the RTT measurements stored are ready to be used by the localization algorithm to infer the MU position.
3.1. Statistical Estimators and Robust Linear Regression in LOS
According to , the range resolution is determined by the bandwidth of the transmitted signal when RTT measurements are used. Furthermore, when using a 44 MHz clock as input of the measuring system to quantify the RTT measurements, the maximum resolution achievable, if only one sample is taken, is about 6.8 m. Moreover, the RTT measurements have a random behavior due to the error introduced by the standard noise from electronic devices, that is always present. To overcome these limitations several RTT measurements have to be performed at each distance and a representative value, called the location estimator, from this group of RTT measurements has to be selected. That selection is based on the model that relates the location estimator to the distance that separates the MU and the AP in LOS.
In this case, as the model is a simple linear regression function, is simply the square of the correlation coefficient, .
The parameters and that characterize the simple linear regression function do not depend on the environment where the wireless localization system is going to be deployed, but on the communication system used, that is, the MU and the AP. These parameters are computed so as to give a best fit of the location estimators to the actual distance. Most commonly, the best fit is evaluated by using the least squares method, but this method is actually not robust in the sense of outlier-resistance. Hence, robust regression has been performed as it is a form of regression analysis designed to circumvent some limitations of least squares estimates for regression models .
The mode ( ) is the value that is most likely to be sampled, thereby it could be a good candidate for the location estimator, but the value that occurs the most frequently in a data set is a discrete value. Therefore, the resolution achieved, , is not enough for indoor localization systems. The same resolution is achieved with the median ( ) as it is a discrete value separating the higher half of a data set. Figures 3(a) and 3(b) show that the Gaussian distributions that characterize the errors of the mode and the median are the widest, with m being and m being .
The mean ( ) is equivalent to the center of gravity of the distribution and it does not take discrete values, thereby the resolution is improved. Although the mean is rather sensitive in the presence of outliers, the use of a robust regression function circumvents this limitation. Figure 3(c) shows that the errors committed when using the mean as a location estimator are characterized by a Gaussian with m, being , lower than the error commit when the median. But Figure 3(d) shows that the best location estimator is the scale-W parameter ( ) once Weibull distribution is fitted to the RTT measurements. In this case is characterized by m and . Therefore, the assumption of a linear function as the model that relates RTT measurements to distance is corroborated by a value of close to the unit. This value indicates that the regression line nearly fits the perfectly.
There is no phenomenological explanation for choosing the scale-W parameter as location estimator of the RTT measurements set, but this parameter is another kind of a location estimator since the maximum likelihood estimator (MLE) of the scale-W parameter is the Hölder mean , a generalized form of the Pythagorean means, taking as parameter the shape parameter of Weibull distribution (for more detail see Appendix A).
where is the estimated distance between the MU and the AP, is the RSS measured in logarithmic units at the reference distance of , is the average RSS in logarithmic units at the actual distance, and is the path loss exponent. According to , for any distance under 20 m in LOS, is recommended to be 2 while for longer distances. Therefore, having taken this value for the path loss exponent and from the RSS values measured between both devices, the distance between the two wireless nodes can be estimated by using the expression (3). In Figure 4 that it can be appreciated the great accuracy obtained by the method presented (square marks), since it outperforms the RSS range based method, specially for cumulative probabilities larger than 50%.
3.2. NLOS Correction
The two sources of range measurement errors in localization techniques are mainly electronic errors and NLOS errors. Electronic errors are inherent to electronic devices and they are commonly modeled as a zero-mean Gaussian distribution. In the previous section, assuming LOS propagation, the effect caused by the electronic error has been minimized by choosing the best location estimator of the RTT measurements, the scale-W parameter. But the assumption of LOS condition is an oversimplification of reality in an indoor environment. Therefore, a method to correct the bias that introduces the NLOS in range measurements has to be implemented to improve the indoor wireless localization system.
The easiest method for dealing with NLOS conditions is simply to place APs at additional locations and select those from LOS, but one of the objectives of this paper is to deploy a wireless localization system in a common and unmodified wireless network. Therefore, the PNMC technique  is implemented to correct the NLOS errors in range measurements. The PNMC technique was created to correct the NLOS errors in open areas. Its performance has been evaluated by simulations in , but as it will be shown in this paper, it also works under indoor environment conditions. Based on a statistical process, the PNMC technique corrects the NLOS effect from a record of range measurements taken through a time window in a previous stage to the positioning process. This processing relies on the statistical estimate of the NLOS range measurements ratio present in the record. The ratio is used to identify the NLOS recorded range measurements. Subsequently, the NLOS range measurements are classified in segments according to the NLOS statistical distribution. Finally, the correction is carried out by subtracting the expected NLOS errors for each segment. For a detailed explanation on the PNMC technique, see .
where the parameter is fixed previously to the localization process.
where is an estimate of the actual MU position.
The second floor of the ETSIT as a real indoor environment with several offices, rooms and many people walking around has been the selected scenario to test the wireless localization system's accuracy. The IEEE 802.11 wireless network deployed in that building has been used as the one over which the MU communicates with their APs whose positions are previously known. The accuracy achieved in the estimation of the MU position is compared for different methods which do and do not mitigate the NLOS errors.
4.1. Experimental Setup
As described in Section 2, the PCB that quantifies the RTT and the localization algorithm are performed in the MU side. It includes an IEEE 802.11b wireless cardbus adapter, specifically a Cisco Aironet AIR-PCM340 with the HFA3861B baseband processor. The wireless adapter has been connected to the computer through a cardbus extender to be able to access to the HFA3861B pinout. This wireless adapter includes two on-board patch antennas with a diversity switch which toggles to and from, and stops when a significant amount of radiofrequency power is detected. The wireless network deployed in the ETSIT building consists of eight identical Linksys WRT54GL IEEE 802.11b/g APs. These APs include two rubber duck omnidirectional antennas in diversity mode that never work at the same time, since diversity circuitry switches to the one with better reception. Rubber duck antennas provide vertical polarization with 360 degrees of coverage in the horizontal plane and 75 degrees in the vertical one. The APs were configured to send a beacon frame each 100 ms at constant power on IEEE 802.11 frequency channel 1 (2.412 GHz).
According to the wireless localization system proposed in this paper, at each position groups of RTS/CTS frames exchange have been performed before computing the boundary . The aim of the parameter is to minimize the use of the IEEE 802.11 channel which could slow down the packet delivery on the wireless network. This parameter is computed at each position according to the confidence interval (CI) explained in Appendix B. The error introduced by the term is corrected by using the PNMC technique, where is observed to be exponentially distributed with m and a time window equivalent to 4 m walking is used. As location estimator, the scale-W parameter has been implemented to reduce the error produced by the term . Finally the multilateration method explained in Section 3 has been implemented to linearize the problem of estimating the MU position.
The performance of the wireless localization algorithm is compared to other cited solutions in an indoor environment to evaluate the goodness of the methods proposed in this paper. They are as follows.
where is the estimated distance by this range method, is the speed of the electromagnetic waves in the media, is the processing time of the AP that is calculated as the mean of the RTT measurements when , is the measuring circuit frequency, and is an empirical estimator of the RTT. Specifically, , where and are the mean and standard deviation of the RTT measurements at each distance, respectively.
(2)The RSS-based range method described by the expression (3) and assuming the paths loss exponent to be for distances shorter then 20 m and for larger distances.
(3)The residual weighting algorithm (RWGH) as a posteriori NLOS error mitigation technique. This technique is based on the sum of the residual squares taking the residual as the difference between the distance estimation and the range between the position estimate and the AP position. For a detailed explanation on the RWGH technique, see .
In Figure 7, green dots correspond to multilateration obtained by using the localization algorithm proposed in this paper. At first glance, these position estimates are highly accurate for the widest corridor. However, they tend to be slightly scattered near APs with numbers 2 and 3 where offices are smaller and corridors are narrower, in other words, where signal suffers from severe multipath. In the same figure, red dots correspond to multilateration obtained by using the empirical RTT-based range method. In order to be an empirical method, its performance depends on the environment in which the MU is going to be located. Therefore, the dynamic conditions of the ETSIT building degrades its accuracy.
From Figure 8 one can appreciate that the empirical RTT-based and RSS-based methods are not suitable for NLOS environments. On the contrary, the proposed localization algorithm without any NLOS errors mitigation technique has a good behavior for NLOS environments with an error lower than 4 m on average. This error is slightly improved after implementing the posteriori NLOS error mitigation technique RWGH. However, the implementation of the PNMC technique achieves the best result with an error lower than 5 m for a cumulative probability of 70%. It is worth pointing out that this precision has been achieved only through multilateration without any tracking technique. Obviously, the positioning accuracy can be improved by using some tracking techniques such as Kalman filters or Bayessian methods, but in this paper it has been indicated that the feasibility of using the wireless localization system proposed in indoor environments without any tracking technique help.
The achievable positioning accuracy of traditional wireless localization systems is limited when harsh radio propagation conditions like rich multipath indoor environments are present. In this paper, a novel RTT-based algorithm to locate devices in such scenarios has been proposed. The wireless localization system proposed has been developed over a hardware solution that performs RTT measurements. The effect of hardware errors has been minimized by choosing the scale-W parameter as RTT estimator. A coefficient of determination value of 0.96 achieved with this estimator in LOS justified the simple linear regression function as the model that relates distance estimates to RTT measurements in LOS. As LOS is not guaranteed in an indoor environment, the accuracy of the proposed localization algorithm has been tested in a rich multipath environment without any NLOS error mitigation technique achieving an error lower than 4 m on average. However, this error is improved after having implemented the PNMC technique to correct NLOS errors. In spite of the multipath fading conditions, experimental results have been shown that the wireless localization system proposed in this paper is highly accurate, achieving an error lower than 4 m in 80% of cases. The algorithm proposed has been compared to an empirical RTT-based and RSS-based localization algorithms and to the RWGH method for mitigating the NLOS effect, concluding that the localization system gives the best results without any tracking technique help that could improve the positioning accuracy.
This research is partially supported by the Directorate General of Telecommunications of the Regional Ministry of Public Works from Castilla y León (Spain).
- Pahlavan K, Li X, Mäkelä J-P: Indoor geolocation science and technology. IEEE Communications Magazine 2002, 40(2):112-118. 10.1109/35.983917View ArticleGoogle Scholar
- Liu H, Darabi H, Banerjee P, Liu J: Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C 2007, 37(6):1067-1080.View ArticleGoogle Scholar
- Gu Y, Lo A, Niemegeers I: A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys and Tutorials 2009, 11(1):13-32.View ArticleGoogle Scholar
- Seow CK, Tan SY: Localization of omni-directional mobile device in multipath environments. Progress in Electromagnetics Research 2008, 85: 323-348.View ArticleGoogle Scholar
- Cheung KW, So HC, Ma W-K, Chan YT: Least squares algorithms for time-of-arrival-based mobile location. IEEE Transactions on Signal Processing 2004, 52(4):1121-1128. 10.1109/TSP.2004.823465MathSciNetView ArticleGoogle Scholar
- Wang X, Wang Z, O'Dea B: A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation. IEEE Transactions on Vehicular Technology 2003, 52(1):112-116. 10.1109/TVT.2002.807158View ArticleGoogle Scholar
- Golden SA, Bateman SS: Sensor measurements for Wi-Fi location with emphasis on time-of-arrival ranging. IEEE Transactions on Mobile Computing 2007, 6(10):1185-1198.View ArticleGoogle Scholar
- Mazuelas S, Bahillo A, Lorenzo RM, et al.: Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE Journal of Selected Topics in Signal Processing 2009, 3(5):821-831.View ArticleGoogle Scholar
- Patwari N, Hero AO III, Perkins M, Correal NS, O'Dea RJO: Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing 2003, 51(8):2137-2148. 10.1109/TSP.2003.814469View ArticleGoogle Scholar
- Bahl P, Padmanabhan VN: RADAR: an in-building RF-based user location and tracking system. Proceedings of the 19th IEEE Annual Conference on Computer Communications, March 2000 2: 775-784.Google Scholar
- Dardari D, Conti A, Ferner U, Giorgetti A, Win MZ: Ranging with ultrawide bandwidth signals in multipath environments. Proceedings of the IEEE 2009, 97(2):404-425.View ArticleGoogle Scholar
- Bahillo A, Prieto J, Mazuelas S, Lorenzo RM, Blas J, Fernández P: IEEE 802.11 distance estimation based on RTS/CTS two-frame exchange mechanism. Proceedings of the IEEE Vehicular Technology Conference, April 2009 1-5.Google Scholar
- Ciurana M, Barcelo-Arroyo F, Izquierdo F: A ranging system with IEEE 802.11 data frames. Proceedings of the IEEE Radio and Wireless Symposium (RWS '07), January 2007 133-136.Google Scholar
- Chen P-C: A non-line-of-sight error mitigation algorithm in location estimation. Proceedings of the Wireless Communications and Networking Conference (WiCOM '99), September 1999, Chengdu, China 1: 316-320.Google Scholar
- Cong L, Zhuang W: Nonline-of-sight error mitigation in mobile location. IEEE Transactions on Wireless Communications 2005, 4(2):560-573.View ArticleGoogle Scholar
- Mazuelas S, Lago FA, Blas J, et al.: Prior NLOS measurement correction for positioning in cellular wireless networks. IEEE Transactions on Vehicular Technology 2009, 58(5):2585-2591.View ArticleGoogle Scholar
- Küpper A: Location-Based Services: Fundamentals and Operation. John Wiley & Sons, West Sussex, UK; 2005.View ArticleGoogle Scholar
- Chan YT, Ho KC: A Simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing 1994, 42(8):1905-1915. 10.1109/78.301830MathSciNetView ArticleGoogle Scholar
- Gast MS: 802.11 Wireless Networks: The Definitive Guide. O'Reilly, Sebastopol, Calif, USA; 2002.Google Scholar
- IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—specific requirements—part 11: wireless medium access control (MAC) and physical layer (PHY) specifications IEEE Std 802.11-2007 (Revision of IEEE Std 802.11-1999), Junary 2007Google Scholar
- Chen VC, Ling H: Time-Frequency Transforms for Radar Imaging and Signal Analysis. Artech House, Norwood, Mass, USA; 2002.MATHGoogle Scholar
- Olive DJ: Applied Robust Statistics. Department of Mathematics, Southern Illinois University, Carbondale, Ill, USA; 2008.Google Scholar
- Weisberg S: Applied Linear Regression. 3rd edition. John Wiley & Sons, Hoboken, NJ, USA; 2005.MATHView ArticleGoogle Scholar
- Borwein JM, Borwein PB: Pi and the AGM: A Study in Analytic Number Theory and Computational Complexity. John Wiley & Sons, New York, NY, USA; 1986.Google Scholar
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