Range validation of UWB and Wi-Fi for integrated indoor positioning
In this paper, we address the challenge of robust indoor positioning using integrated UWB and Wi-Fi measurements. A key limitation of any fusion algorithm is whether the distribution that describes the random errors in the measurements has been correctly specified. Here, we describe the details of a set of practical experiments conducted on a purpose built calibration range, to evaluate the performance of commercial UWB sensors with Wi-Fi measurements as captured by an in-house smartphone application. In this paper, we present comparisons of ranges from the UWB sensors and the Wi-Fi built into the smartphone to true ranges obtained from a robotic total station. This approach is validated in both static and kinematic tests. The calibration range has been established as one component of an indoor laboratory to undertake a more diverse research agenda into robust indoor positioning systems. The experiments presented here have been conducted collaboratively under the joint FIG (WG5.5) and IAG (SC4.2) working groups on multi-sensor systems.
KeywordsUltra-wide band (UWB) Wireless Fidelity (Wi-Fi) Differential approach Indoor positioning Smartphone localization Performance comparison System integration
Localization capabilities are nowadays standard features in mobile devices leading to the fact that society has become increasingly reliant on a location-enabled lifestyle (Saeedi and El-Sheimy 2015). Seamless precise navigation in combined outdoor and indoor environments, however, is still a great challenge (see, e.g., (Gikas et al. 2016b; Kealy et al. 2015; Toth et al. 2017)). Application specific requirements of indoor positioning systems (IPS) create the baseline for the performance needs of the solutions under development. Extensive reviews of relevant research have been performed and vary from covering the requirements of navigation and tracking for emergency responders’ applications (Fernández and Schön 2017) to pedestrian IPS for mass-market devices (Chen et al. 2015; Correa et al. 2017) and indoor parking modeling (Antoniou et al. 2018; Gikas et al. 2016a). Depending on user requirements and specific case scenario needs, a wide range of localization techniques and technologies is currently available ranging from optical to inertial and radio-based ones. For instance, pedestrian dead reckoning (PDR) exploits the benefits of inertial sensors due to their self-contained functionality (Chen et al. 2015). Similarly, localization problems featuring a group of users employ collaborative positioning techniques while radio-based technologies play a dominant role in the solution provided (Fernandez and Schön 2017; Kealy et al. 2015). In terms of techniques, in addition to traditional approaches, such as angle and range lateration, signal strength/fingerprinting techniques expand rapidly thanks to the advancements in the information technology sector (Gikas and Perakis 2016). Furthermore, new computational approaches that make use of artificial intelligence support the development of map-matching and SLAM solutions (Zampella et al. 2015; Zandbergen and Barbeau 2011).
In the last 5 years, the authors of this paper have developed in a number of studies and proposed new concepts for indoor localization, particularly for personal mobility applications (Gikas and Perakis 2016; Hofer and Retscher 2017; Retscher et al. 2017). These investigations have led t o a considerable improvement of localization performance in the proposed approaches. In this paper, the concepts developed in previous studies are combined together to even strengthen the navigation solution. To evaluate the achieved performance practical testing was therefore carried out in an indoor lab setting.
The paper is organized as follows: In the “UWB positioning” section, first the characteristics of positioning using ultra-wide band (UWB) are identified followed by brief description of a differential approach for Wi-Fi (Wireless Fidelity) positioning in the “DWi-Fi positioning” section. The “Field-testing in an indoor laboratory” section presents the field test site in a laboratory in the basement of a multi-story office building. Then, the “UWB and Wi-Fi integration scenario” section elaborates briefly the integration of UWB and Wi-Fi to strengthen the navigation solution. Finally, brief conclusions are drawn and an outlook on future work is given in the “Conclusions and outlook on future work” section.
In addition to DWi-Fi positioning, (Retscher and Hofer 2017) have developed an approach to include waypoints along the trajectory of the user which are chosen in an intelligent way into the overall navigation solution. This development was referred to as intelligent checkpoint (iCP) concept by the authors. The principle of operation is that waypoints are selected along the users’ trajectory under consideration of their dependence following a logical sequence when navigating from a start point A to a destination B. These iCPs are located on well distinguishable decision points. In the case of indoor positioning, first an entrance has to be chosen when coming from outdoors into a building and then one will enter a foyer or similar area. To reach the next floor, either the stairs or an elevator must be used. Before one can reach the destination room, one has to walk along a corridor from the previous waypoint. Doors, stairways, and corridors can be considered as iCPs which define the possible path. For the identification in indoor environments, a building is then divided into different sections and layers (Hofer and Retscher 2017). The further one enters the building, one reaches a deeper layer of the allocated vector graph. The categories which already describe an easy logical sequence can be derived from these layers. A solution is that all entrances of the building are combined into the first layer. The final layer contains all destination rooms on a certain floor. Then, these layers represent how far one has already progressed inside in a building. If one follows the layer structure from the beginning, only one certain choice of waypoints is always available. To be able to use these logical relationships, however, it is necessary to recognize certain waypoints in a building. With a suitable choice of these iCPs, the structures of the building are usable since in buildings, different bottlenecks must be passed over and over again to reach the destination. These bottlenecks include also structures, such as walls, corners, or doors, which can influence the measured Wi-Fi RSS to the available APs significantly, i.e., much higher or lower values of the RSS can then be measured on different iCPs.
Extending the two concepts described above—DWi-Fi and iCP detection (Retscher et al. 2017) presented the integration of these techniques as well as inertial navigation (IN) using the smartphone sensors (accelerometer, gyroscope, and magnetometer). They could show that an integrated solution yields a satisfactory performance with achievable positioning accuracies of better than 1 m. In the following section, the results of UWB and Wi-Fi positioning are presented followed by a discussion of their integration to strengthen the navigation solution in the “Conclusions and outlook on future work” section.
Field-testing in an indoor laboratory
In this section, firstly, the test site of the experiments with its setup is presented followed by the achieved results for UWB and Wi-Fi positioning.
Start point ID
D6603 Sony Z3
GT-I9300 Samsung S3
SM-G930F Samsung S7
GT-I9295 Samsung S4
GT-I9300 Samsung S3
In the tests presented here, user 1 walked along a trajectory from point 1 to 103 via 104, 2, and 5. The other four smartphone users remained stationary. Ground truth for this experiment was measured using a robotic total station providing position fixes on the cm accuracy level.
Results of UWB positioning
Time Domain ® PulseON 410 UWB modules generate three types of range measurements using coarse range estimation (CRE) which is based on relative signal strength of the first received pulse, precise range measurement (PRM) which is the outcome of the TW-ToF range estimation and the filtered range estimate (FRE) which is the outcome of an internal Kalman filter implementation of the P 410 module combining the CRE and PRM ranges (Dewberry and Beeler 2012). For the validation purposes of the current study, the PRM ranges functionality is selected as the most unaffected outcome of the modules in order to avoid possible filter generated impacts on the FRE range and the instability of the CRE ranges.
Results of Wi-Fi positioning
For the conversion of the RSS into a range, path loss models are required. A suitable model is the so-called one-slope model. It is a very simple empiric model which is based on the principle on the free space loss of the signals. The damping of the signals depends then only on the logarithmic distance between the transmitter and receiver and the reference RSS in the following form:
where P is the received empirical RSS, P0 the reference RSS in 1-m distance, γ the damping factor, and d the distance between the transmitter and receiver.
Limitations of the obtained UWB results
Regarding the low-accuracy results of the UWB trajectory along the X axis, the major impact is attributed to the poor geometry of the anchor UWB node positions along the corridor covering in an asymmetrical manner the test area. The long and narrow design of the test area in combination with the requirement for Line-of-Sight (LoS) conditions guided the anchor point selection. However, the current results point to the necessity of evaluating the placement of an additional UWB outside the test area for improving the overall geometry. At the same time, a Non-Line-of-Sight (NLOS) ranges weighting technique should take place for mitigating any through-wall signal attenuation effects.
UWB and Wi-Fi integration scenario
In the experimental work discussed in this study, the role of the UWB system can take two forms depending on the specific goal. Firstly, to provide a high-quality trajectory to serve as a ground truth for the DWi-Fi solution, and secondly to provide further location information (e.g., in the form of control points along a trajectory) for improving the DWi-Fi positioning solution. In this paper, the UWB measurements are used only to produce the range measurements between the moving user and stationary UWB nodes, as a means of quality assurance of the mobile user trajectory. In the future extension, an integration of the UWB and Wi-Fi ranges to the respective anchor nodes and APs is performed for trilateration. Thereby, a meaningful weighting depending on the quality of each range has to be applied. This approach will strengthen the overall navigation solution.
Further integration of the UWB/DWi-Fi approach would assume using MEMS IMU information currently available in contemporary smartphones (Gikas et al. 2016b). The in-house developed App (Hofer and Retscher 2017) also records the data of the inertial smartphone sensors. Thus, continuous positioning of the users is enabled augmented by the UWB/DWi-Fi solution serving as absolute localization techniques. This comes along with further challenges, such as adaption of a suitable sensor fusion approach based on an EKF coming along with a meaningful weighting of all observations as well as time synchronization of the sensors with the absolute positioning solution.
Obviously, such a navigation scheme assumes the adoption of near real-time processing tools and thorough quality control procedures to accommodate with the incoming information from disparate data sources. Other applications could benefit from additional sensor types. For example, an approach to large-scale indoor parking facility management would reside on a low-cost RFID Cell-of-Origin (CoO) solution complemented by UWB/DWi-Fi (Gikas et al. 2016a; Hofer and Retscher 2017). In this case, the DWi-Fi would guarantee broad coverage while the UWB would increase the overall system accuracy and robustness thanks to its high-accuracy potential and foreseeable decreasing costs.
Conclusions and outlook on future work
In this paper, the integration of DWi-Fi and detection of waypoints, the so-called iCPs, with UWB range measurements for navigation along the users’ trajectory is discussed. In the case of DWi-Fi reference stations (RSs) are deployed where continuous RSS measurements of all visible APs and other RSs are performed. Further information on the operational principle of this approach can be found in (Retscher et al. 2017).
Experiments were conducted in a lab setting. The results in this study demonstrate the performance of UWB and Wi-Fi for range validation. One limitation of the tests, however, was the bad geometry of the location of the UWB anchor nodes as the lab has a length over nearly 60 m and a width of only around 5 m. Also in the case of Wi-Fi positioning, the intersection for trilateration from the ranges to the Raspberry Pi units was not optimal.
Further testing in combined out-/indoor environments was conducted in a new measurement campaign at the Ohio State University, USA, in the first week of October 2017. This work is carried out from the joint FIG (WG5.5) and IAG (SC4.2) working groups on multi-sensor systems. Currently, the described approaches in this paper are applied to these measurements.
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