WiFi positioning with estimated ranges using Round Trip Time (RTT) measurements based on IEEE 802.11 Wireless Local Area Network (WLAN) has become well known since Fine Timing Measurement (FTM) protocol has been characterized. However, the multipath efect is one of the barriers to accurate time-based range measurement. On the other hand, Ultra Wide Band (UWB)-based range measurement has fair resistance to multipath efects but its accuracy is highly dependant on the orientation of the antennas in the transmitter and the receiver and its transmit power is also limited due to the applied regulations. This paper utilizes a Received Signal Strength (RSS)-based fusion of both UWB and WiFibased range measurements to increase the indoor positioning accuracy. The proposed method takes the advantage of WiFi FTM protocol as well as Two-Way Ranging (TWR) for UWB devices. The empirical range measurement campaign is done at the University of Helsinki premises. Test points with known positions are considered as the ground truth to evaluate the results. The outcome proves that fusing UWB and WiFi ranges for indoor positioning, improves the accuracy in comparison with using the UWB or WiFi alone.
Signals-of-opportunity (SOOP) provide a good means for positioning indoors, where Global
Navigation Satellite System (GNSS) signals are not available [
UWB-based positioning is a rapid developing field regarding the accuracy and the
robustness it provides [11]. The accuracy in both static and dynamic positioning on reference test
points are investigated in [12] and [13]. The static position investigation is done to analyze
the ranging capabilities in a favorable environment. Static positioning can also be used in
Wireless Sensor Network (WSN) nodes positioning, where the sensor nodes are fixed [ 14].
The range measurement in static condition using Pozyx devices is also addressed in [15]. The
results indicate that the UWB-based range measurement can have errors as big as 1.73 and
positioning can have errors more than one meter in a dense indoor environment where many
objects are causing multipath efects. This error can be increased to 5.919 when the user
is dynamic in a narrow corridor [13]. Furthermore, the accuracy of UWB devices where the
antenna is omnidirectional is highly dependent on the relative orientation of the transmitter
and the receiver [
In this work, considering the UWB and WiFi RTT-based positioning, we focus on mitigating
the efects of the above-discussed issues, including the limitation of transmission power and
orientation-dependent accuracy of the UWB TWR, as well as multipath and position-dependent
errors with the WiFi RTT. We address the error in the WiFi RTT device, present an RSS-based
fusion of the UWB TWR and the WiFi RTT, and evaluate our work by using multilateration for
static positions [
The remainder of the article is divided into the following sections. Section 2 explains the theoretical background for range measurements and multilateration. The methodology of deploying the range measurement, correcting WiFi position-dependent error, and RSS-based fusion method are presented in Section 3. Section 4 provides the experiments and results, followed by the Section 5 concluding this work.
Radio Frequency (RF) signals are widely used for indoor positioning purposes. In this work, we
address trilateration using radio-based range measurements from three anchors. Regarding the
indoor environment, we investigate the two candidate signals [
Regarding the rapid use of WiFi signals in indoor positioning, FTM protocol enables an initiating device like a smartphone to find its distance to a WiFi base station like an Access Point (AP). The process begins when an initiator (it can be a mobile phone) sends an FTM request to an AP. If the AP supports the FTM process and can respond, it is called a Responder. The Responder may agree or refuse the initiator’s message for the ranging procedure. If it agrees with the message, it will send an acknowledgment message (ACK) to the initiator. Then the Responder starts sending the initiator an FTM message and waits for the ACK from the initiator. The AP is allowed to send the next message only when it receives the ACK from the initiator. This procedure is illustrated in Fig. 1.
Considering the transmission timestamp of the FTM message and the reception of the ACK in the ℎ procedure of a burst, RTT is estimated:
Initiator (Smartphone)
Responder
(AP) 2,1 3,1 2,2 3,2 2,3 3,3 1,1 4,1 1,2 4,2 1,3 4,3
FTM Interchange 1
FTM Interchange 2
FTM Interchange 3 where, 4, is the receiving time of ACK, 1, is the transmission time of the FTM message, and 3, − 2, is the processing time in the mobile phone, as illustrated in Fig. 1. Several FTM-ACK processes occur in one burst. The RTT calculation continues for all the FTM-ACK processes in the burst and finally, to report the RTT, the average value will be considered.
All the timestamps are transmitted to the mobile phone to calculate the RTT. In this way, the
user is enabled to estimate its position based on RTT-based ranges while its privacy is preserved
[
The trilateration method uses triangles geometry to estimate the target position of the mobile
objects. For estimating the 2D position using multilateration, at least three base stations/anchors
are required [
(x3,y3)
R2 (x2,y2) Base station with known location
User with unknown location Moreover, (1, 1), ..., (, ) are the known positions of the anchors, and 1, ..., are the range measurements from the user to the anchors.
UWB is a microwave signal comprising electromagnetic radiation within a frequency range
from 3.1 to 10.6 . UWB signals are shown to propagate through walls and diferent
materials [19]. Range measurement using UWB can be done with diferent techniques relying
on time measurements such as Time of Arrival (ToA), Time Diference of Arrival (TDoA), and
TWR. In this paper, the considered TWR approach depends on the time that RF signal requires
to travel from the transmitter to the receiver, to be processed, and travel back to the transmitter.
In this way, synchronization issues are conveniently solved in comparison with ToA and TDoA
method, [
Time-based measurement can be divided into three principles including continuous-wave, impulse radio, and pseudo-noise modulation. The most commonly used method is the impulse radio. In this method, to have accurate timing a large bandwidth is required to have a narrow pulse since wide pulse results in capturing all the reflections from the environment while the signal is scattered onto the objects such as walls [12]. Regarding the fact that in UWB systems the pulse width is in order of nanosecond since the bandwidth is around 500 , UWB can filter reflections of the signals to a great extent. However, the interference potential with conventional radio systems should be avoided by limiting the power spectral density. Therefore, regarding the decision of the Federal Communications Commission (FCC) in the USA and the Harmonized European Standard (EU ETSI EN 302 065), the maximum allowed isotropic radiated power density for unlicensed use of UWB is limited to − 41.3 / .
In the next section, we describe how we utilize the smartphone and the Pozyx developer tag to collect the range measurements.
In this paper, we are using both UWB and WiFi anchors for obtaining the desired position estimates. To reach our goal, we have put a pair of UWB and WiFi anchors aligned on each other in three known locations, and the ranges from the user equipment to the anchors are calculated with both UWB and WiFi devices separately. The instruction of setting up the system is illustrated in Fig. 3 for the user equipment and one of the anchors.
Connected to a laptop
User Equipment (Unknown Location)
UWB (Pozyx Tag) (saving UWB TWR
on laptop) Google Smartphone (collecting WiFi RTT) Tag and Smartphone
Aligned
UWB-based Range measurement WiFi-based Range measurement
To make the WiFi-based and UWB-based positionscomparable
UWB (Pozyx Anchor)
WiFi WILD UWB and WiFi Anchors
Aligned
Anchor (Known Location)
The range measurements are collected in the reference points and averaging is used for the collected data at each point.
To collect UWB range measurements, Pozyx developer tags are used. These tags have a Decawave DW1000 transceiver, which can operate within a frequency range from 3.5 to 6.5 , and the power gain can be modified up to a maximum of 33 dB to control the output power. However, to assure that the signal transmitted power density stays below the maximum allowed power, the Pozyx tags average transmit power gain is set below 20 . The ranging method used for finding the distances between the Pozyx tag and the anchor is TWR. The Pozyx tag at the user equipment is connected to a laptop to save the range measurements on the laptop and get the timestamp from the operating system.
To collect the WiFi RTT measurements, we used a Google Pixel 3 smartphone to receive the data from WLAN anchors. This phone can support the Android RTT API as the receiver to collect the range measurements [17, 18]. The WiFi ranges and the timestamps are collected and saved in the smartphone.
By investigating the WiFi range measurements, we recognized that there is a non-constant error value for the measured ranges. Based on [21], there are two equipment-related issues with the WiFi devices. The first one is an ofset value in the reported range measurements, which is quite repeatable and can be estimated by taking many measurements in known distances and calculating the average value. The second one is the position-dependent error in the measured ranges. To solve the latter, several measurements of the range should be done in an environment without any obstruction and find a linear fit to model the error [ 22]. Thus, we have done a range-measurement test inside the Anechoic Chamber of the Department of Computer Science at the University of Helsinki. The Anechoic chamber is a room that absorbs electromagnetic reflections and is isolated from interfering signals [ 23]. The Anechoic chamber of the department has a size of 250 × 240 × 400 and is constructed using the Rainford EMC Systems modular panel system. The whole chamber, including the sidewalls and the ceiling, is covered with 30.5 thick, solid, sharp-tip, resistive pyramidal foam absorbers. The chamber is shown in Fig. 4.
To do the experiment and analyze the ofset and position-dependent errors, we have fixed one of the WiFi devices in the chamber and put the Google Android Phone in diferent distances from 0 to 200 with 10 increasing steps. We collected the range measurements with the Android phone for three minutes at diferent distances. Then we averaged the collected range measurement in each known distance to model the distances. Least-squares line fitting is used to estimate the calibrated distances based on the collected RTT-based ranges. The linear model is shown in Fig. 5, where the fitted first-order polynomial is defined as: = 0.614 × + 4.266 (6) Using this model, we were able to estimate the correct ranges with a mean error of 35 .
To fuse the WiFi and UWB range measurements, we have first synchronized the smartphone and the laptop (to which the Pozyx tag is connected) by Network Time Protocol (NTP). NTP is commonly used to synchronize the clock of network devices within the accuracy of a few milliseconds. By synchronizing the smartphone, which collects the WiFi RTT, with the laptop, which collects the UWB TWR measurements, we can fuse the measurements occurring with the same timestamp.
We take the advantage of corresponding RSS values for each range measurement. Regarding
the fact that path loss and attenuation result in decreased RSS values [24], and knowing that
very high RSS, in many environments indicates LOS dominated channel[
1 = ( ∑︀=1() ) × 22 ×+ 2 2 = ( ∑︀=1 () ) × 22 × + 2 (7) (8) where and are the corresponding RSS values for each range measurement in , is the update rate, ∑︀=1 () and ∑︀=1 () are the sum of RSS values at each time epoch for UWB and WiFi, respectively.
In the definition of 1 and 2 the efect of RSS and accuracy are applied. In one time epoch, the number of available measurements equals the update rate of the device . Considering the processing of data after each time epoch while we have all the measurements occurring in one epoch, we can define the fraction ∑︀=1() for each measurement. Larger RSS values imply a stronger signal and result in larger 1 and 2 values. In the second fraction of the equations (7) and (8), the efect of accuracy is applied. According to ISO 5725-1, accuracy is defined by two factors: trueness and precision, where trueness is the mean and precision is the variance of the error distribution. For applying the two factors in the RSS-based fusion, we have first found the mean and variance of the estimated position errors using UWB ( , ), as well as those of the estimated position errors using WiFi ( , ). Then we have defined the weights based on the mean of a Gaussian Probability Distribution Function (PDF) which is the product of the two (WiFi and UWB) Gaussian PDFs.
Finally, the weights will be calculated using (9) and (10).
(9) (10) (11) = =
1 1 + 2
2 1 + 2 Thus, the final fused range value would be:
= × + × where represents the collected range measurements with WiFi devices, which are calibrated by using (6), and represents the collected range with the UWB devices.
To test the algorithm and evaluate our work, a measurement campaign is done at the premises of the Department of Computer Science, University of Helsinki. The floor plan of the area for the test is illustrated in Fig. 6.
Anchors Reference Points
18 reference points with known locations are considered. The reference points are marked with yellow signs as illustrated in Fig. 7 and 8.
The WiFi anchors are Compulab WILD WLAN devices that are capable of providing RTT measurements. The UWB anchors are Pozyx Developer anchors. The aligned UWB and WiFi anchors in one of the base stations locations are illustrated in Fig. 9.
For the UWB modules, channel 5 is selected, preamble length is 1024, Pulse Repetition Frequency (PRF) is 64 , bitrate is 110 , and transmit power is 19.5 .
The Pozyx tag is connected to a laptop for UWB TWR collection using a developed Python code. Furthermore, the smartphone using a developed Android application is utilized for WiFi RTT collection. The phone and the tag are illustrated in Fig. 10. The range data is collected once using the synchronized smartphone and the laptop. The UWB and WiFi measurements are used both separately and fused for estimating the positions.
Using the range measurements collected at the test points, we have first done the position estimation using WiFi-based ranges and the trilateration method. The estimated positions are illustrated in Fig. 11.
The calculated mean error of the estimated positions using only the WLAN devices is 133.9 .
After using the WiFi-based ranges, we have used trilateration for UWB-based range measurements. The estimated positions using only UWB TWR are illustrated in Fig. 12.
Estimating the positions using only the UWB devices resulted in a mean error of 111.4 .
After investigating the UWB and WiFi-based position estimations, we have fused the range measurements using the RSS-based algorithm presented in the methodology section. By using the fused range measurements, we were able to decrease the mean error to 75.7 . The estimated positions using the fused UWB and WiFi-based range measurements are illustrated in Fig. 13.
The results prove that UWB and WiFi fusion presents sub-meter level accuracy in a dense multipath indoor environment where the WiFi signals are experiencing reflections as well as position-dependent errors, and the omnidirectional antennas of Pozyx UWB devices are not -400 -400 necessarily heading toward each other. As summarized in Fig. 14, compared to individual UWB and WiFi-based measurements, the mean and Standard Deviation (STD) of the positioning error are decreased by 32 − 43% and 39 − 51%, respectively, by fusing the UWB and WiFi measurements. -400 -100 160 140 120 100 80 60 40 20 0
Errors in Position Estimation (cm)
In this work, the WiFi RTT-based ranging and UWB TWR are addressed using Compulab WLAN and Pozyx UWB devices. The range measurements are first used in the multilateration algorithm to estimate the position solution separately for the WiFi and UWB. Regarding the problem of multipath efect which is highly efective on WiFi RTT-based ranges and the problem of orientation-dependent accuracy of UWB as well as transmit power limitations by regulations, an RSS-based fusion of the range measurements is used to investigate the superiority of the integrated ranges for the position estimation. The results confirm that the accuracy of estimated static position solutions is improved in the real indoor environment by fusing the UWB and WiFi-based range measurements. In the next steps of this research, specific noise probability distribution in the range measurements will be addressed. In addition, a mobile user with appropriate tracking algorithms is considered for a navigation application. Besides the above, it is important to investigate a larger positioning area with additional reference points for more comprehensive testing of the system behavior.
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