- Research Article
- Open Access
Experimental Data Collection and Performance Analysis of Outdoor UWB Positioning System under Static and Mobile Conditions
© P. Richardson and D. Shan. 2009
- Received: 16 August 2009
- Accepted: 15 December 2009
- Published: 8 February 2010
A wide variety of semiautonomous systems are emerging in construction, defense, and agricultural applications. A UWB positioning system shows promise in improving navigational capabilities and safety of operations for these systems. This paper describes an outdoor UWB positioning system used to measure the position of the operators of a semiautonomous vehicle to improve safety of operations. A measurement campaign was conducted to collect experimental range errors for the system at distances from 2 m to 40 m. The range errors are characterized and performance of the system is assessed in a static environment. A model is proposed for range errors and results are compared to experimental data. Measured position errors are compared to position errors generated by the model. A mobility model is proposed and performance of the positioning system in a mobile environment is assessed.
- Medium Access Control
- Position Error
- Packet Loss Rate
- Experimental Data Collection
- Analyze Position Error
Ultra-wideband Impulse Radio (UWB-IR) systems are able to achieve fine time resolution using very narrow pulses. Accurate time measurements yield accurate range measurements that provide a building block for positioning systems, a capability emerging in a diverse range of applications [1–5]. In this effort we present an analysis of range and position measurement accuracy based on experimental data for a UWB-IR positioning system developed for outdoor environments. Outdoor UWB positioning systems provide an important capability for emerging semiautonomous applications in defense, construction, and agricultural vehicles. Accurate position measurements with high update rates can be used to enhance navigation, especially in GPS denied environments such as deep canyons and heavily forested terrain. Positioning data can improve safety of operations by providing operators and vehicle control systems with position information of nearby objects. This data can also facilitate certain autonomous applications such as "leader-follower" wherein a trailing vehicle will automatically follow a designated operator or vehicle.
Safety limitation for platform speeds near operators.
distance from nearest operator less than 2 meters
maximum platform speed 0 m/s
The UWB positioning system uses commercially available programmable UWB radios and achieves accurate positioning at distances up to 40 meters with 20–25 Hz update rate for each position measurement. The UWB radios are programmed and calibrated to optimize for high update rate and accurate range measurements in an unobstructed path. An integrated controller collects range data, calculates position measurements, and controls the system's steering, braking, throttle, and transmission. The system provides 360 coverage for up to five operators at distances between two and forty meters from the vehicle's center. This information is used to improve safety by limiting vehicle speeds based on proximity to operators. The main focus of this effort is the analysis of UWB range errors and update rate based on an experimental data collection and the ensuing impact on position measurements in static and mobile conditions. In this initial phase, we analyze performance in a flat open grassy field and in an urban environment at distances up to forty meters. Future phases will address more complex channel environments such as hilly terrain and obstructed channels.
1.1. Previous Related Work
Increasing interest is shown towards UWB positioning and tracking systems [1–5]. The main focus of such systems has been for indoor environments, with scant data available for outdoor systems at longer ranges. UWB tracking has been explored for space applications [1, 2], commercial asset tracking [3, 4], and assistance to disabled persons . Mahfouz et al.  describe a UWB indoor local positioning system that uses a 300 ps Gaussian pulse modulated on an 8 GHz carrier to provide accurate range measurements at short ranges in an indoor environment. The author analyzes errors caused by multipath interference, sampling rate limitations, tag synchronization, and antenna phase variations. Experimental data is presented and a path to achieve very high accuracy in an indoor environment is described. In  a report of an indoor range measurement experiment using UWB IR radio is presented for LOS and NLOS intraroom propagation conditions.
Several projects [8–17] have addressed important issues regarding position estimation techniques. In  position estimates based on TOA signals are presented using classical filtering and an alternate approach that does not require knowledge of range error distribution. A novel joint position estimation approach using time-of-arrival (TOA) and direction-of-arrival (DOA) integrated with an extended Kalman Filter is proposed in  to achieve robust positioning. In , analysis of TOA-based estimation mechanisms is provided that addresses design of receiver architectures for UWB systems and provides simulation of the architectures using the 802.15.4a channel model. In , the authors propose an adaptive combining approach to reduce position measurement uncertainty. The approach combines TDOA measurements and uses directional motion increments to reduce random channel noise and dilution of precision (DOP). The approach shows promise to improve performance over existing methods because of its low cost and battery life impacts. NLOS channels are studies in [12–15]. In , the authors address challenges for indoor NLOS channels using a hybrid TDOA/AOA positioning approach. An analysis using modified extended Kalman Filter (MEKF) & modified regularized particle filter (MRPF) for position estimation in a mobile indoor environment is provided in . In  the authors propose the use of RMS delay spread to detect NLOS propagation conditions which enables effective use of algorithms developed to alleviate NLOS propagation affects. In  the authors evaluate the performance of particle filters to improve raw position errors for a hybrid GPS-UWB system to track vehicles on the highway and in warehouses. The GPS is used for tracking outdoors and a UWB system is used to track within warehouses. In  the authors evaluate the range error of a pulsed UWB system, concluding that accuracy at longer distance depends on pulse spectrum and that short transmit pulses are necessary in dense multipath environment to maintain accuracy.
This effort presents experimental data for an outdoor, long range UWB positioning systems that have not previously been reported in the literature. We present the accuracy of range measurements based on experimental data collection and the ensuing impact on position measurements in static and mobile environments. A model based on Gaussian distribution of range errors is proposed and compared to experimental results. The fundamental tradeoff between range accuracy and range update delay in a mobile environment is discussed and simulation results are presented. Estimation and prediction are core issues for improving positioning and tracking performance that are also related to the mobility model of the system. The detailed discussion of these issues and the study of more complex channel environments are beyond the scope of this paper and will be the subject of studies in the near future.
In the next section, an overview of the UWB radios and tracking system is provided. Section 3 presents the characteristics of the range measurement error based on experimental data collection and discusses the impact on position measurement error in a static environment. Section 4 presents a mobility model and assesses the impact of target motion on position measurement errors. Section 5 provides a summary and identifies areas of future work.
This section provides an overview of the UWB positioning system from the perspective of understanding the characteristics of the errors and delays in measuring range and their affect on position measurements in static and mobile environments.
2.1. UWB Radio Description
The system uses the P210 radio from Time Domain, a time hopping, UWB-IR with capability for extended range measurements in outdoor environments. The radio transmits a pulse train with a 9.6 MHz pulse rate and time hop code of length 16. The pulse width at the internal SRD diode is 0.235 nanosecond and has a center frequency of = 4.26 GHz. The pulse is high pass filtered in order to conform to the FCC mask for UWB at 3.1 GHz. Measured 10 dB frequency response of the radio is from 3.1 GHz to 5.8 Ghz. The radio can operate as a conventional radio transmitting data packets or it can send special 1/2 duplex range packets that measure time delay between any two radios. Higher layer services for data link control or networking must be provided by the user. The radio supports a 40 MHz Strong Arm Processor with 32 MB RAM and an Ethernet 10BaseT interface to host higher level services or user applications. The radio has a wide range of parameter settings that provide flexibility for making tradeoffs such as transmission delay versus reliability and range update rate versus range accuracy. A description of tradeoffs for several important parameters is provided in Section 3.
The P210 data packet consists of a synchronization preamble and payload data segment. Additional segments to support upper layer services (e.g., MAC or Network) can be added to the payload segment by the user. The parameter settings for synchronization affect the tradeoff between packet loss rate, maximum distance, and transmission delay. The primary parameters for synchronization include pulse integration, correlator sampling interval, and threshold settings. During packet synchronization, the radios have I-Q correlator pairs that sample at the time hop intervals. The correlator outputs are integrated and compared to a threshold that detects acquisition. The correlator sampling interval is adjustable and has a default step size of 235 ps or approximately one pulse width. The symbol energy, , can be increased during synchronization using a pulse integration process, wherein the number of pulses that represent a synchronization symbol is increased as a power of two from 2 to 2 . Each increment in pulse integration doubles the accumulated signal voltage, providing a 6 dB increase in signal power. Gaussian noise increases by a factor of 1.41 dB for a 3 dB increase in power. Thus each doubling of integration yields approximately 3 dB increase in signal-to-noise ratio (SNR). However, increasing integration also increases the transmission delay. The number of synchronization symbols is adjustable and the default value is 443 symbols, which allows the correlators to sample (step) over one pulse interval of 104 nanoseconds searching for a valid preamble as shown in (1),
where is the pulse period and is the correlator sampling period. The transmission delay for the synchronization preamble is given by
where I is the synchronization integration, is the number of synchronization symbols, and is the pulse rate. The receiver threshold can be set either manually or automatically. The automatic mode adds an additional symbols to the synchronization preamble. The correlator outputs over the initial symbols are used to estimate SNR and the synchronization threshold is set based on the estimated SNR. Autothresholding is effective in a dynamic channel environment but increases synchronization delay by 50%. The various methods to reduce packet error rate (e.g., increasing I, reducing , automatic thresholding) and the accompanying increase in transmission delay defines one of the key tradeoffs in mobile positioning accuracy that must be carefully studied.
2.2. Range Packets and Measuring Range
The coarse time stamps and are taken when the correlators achieve acquisition. Depending on the channel response, this may not be the leading edge of the pulse.
2.3. Position Measurements
The radios on the vehicle represent coordinate nodes that obtain the range to the operator and send range measurement and status information to the controller. The controller monitors and controls the system and also collects range data and determines positions. The coordinate radios and controller share information using UDP/IP packets over Ethernet. Mean UDP packet delay was measured at 270 microseconds with a variance of 40 microseconds. Since range delays are on the order of s, the UDP packet delays are not a primary consideration.
and that this area and hence position error decrease as increases from and it is minimized when . Decreasing either the separation distance between the target platform or will cause to increase. The increased position error due to decreasing is known as geometric dilution of precision (GDOP) and has been well studied .
Factors, such as primary direction of travel and safety restrictions, can be used to further refine zone boundaries. DOP is well studied  and detailed discussion of DOP and zone coverage is not provided in this paper.
where the position in two coordinates is defined by . If ambiguity is introduced because of position measurement errors and then historical position data or a third range is used to determine the correct measured position. If a range packet is dropped or detected to have a large error (outlier), then additional ranges can be requested. In practice, the number of retries is limited by an algorithm that looks at the delay impact to the target update rate for all other targets.
2.4. MAC and Tracking Multiple Targets
Media access control (MAC) is required to coordinate range packets between various radios. Since the system is inherently centralized about the platform, we opt for a centrally controlled, polling MAC that resides on the controller and uses polling time slots to track each target. The controller uses past position measurements to determine which coordinate radios and target will be involved in the next polling event. In the initial phase, polling is round robin between targets. Processing time for various filters and bounds checking is less than 1 milliseconds. If we assume 5 targets with two range measurements for each position and a delay of 20 milliseconds per range, then the system will have an overall update rate of approximately 5 Hz per target.
An experiment was conducted to establish a baseline assessment of performance limitations for range accuracy under the simplified conditions of an unobstructed channel on level terrain during the initial development of the system. Range data was collected in an open grassy field and in an urban environment at distances from 2 m to 42 m in 2 m increments. The heights of the transmit antennae, , and receive antenna, , were both set to 1 m. Truth points were established using a high precision laser with a mean accuracy of 0.002 m and a variance of 0.001 m. At each truth point, 4000 range samples were collected and the individual range measurements, number of dropped range packets, outliers, and successful range measurements were recorded.
Generally, the attempt to measure a range can have one of three results: a dropped range packet, a large range error, also called an outlier, or a successful range attempt. A dropped range packet occurs when synchronization is not achieved over the preamble and the range attempt fails. This occurs when the received SNR is insufficient, either due to path loss or fading. A large range error can be caused by fading or by a system problem, such as the scan window being too small relative to channel delay spread. Typically outliers have a measurement differential from the preceding range in excess of several meters. In our static data collection, we set a threshold of 1 meter for outliers. A successful range attempt will have some small error associated with fading or uncertainty in leading edge detection (LED).
Preliminary experiments were conducted to evaluate a wide range of parameter settings. Parameters were selected that balanced accuracy of range measurements against time duration to complete a ranging operation. Automatic synchronization thresholding was selected, as experiments verified that it is not possible to reliably cover distances from 2 m to 42 m using a static threshold. Experiments with correlator step size show significant increase in packet loss with step sizes greater than the default 235 ps. The synchronization integration was selected in order to minimize the effects of path loss at a maximum range of 40 m. The synchronization parameters for integration and step size were set to 64 and 235 ps, respectively. The received symbol must have enough energy, to overcome path loss effects at d = 40 m. For analysis of path loss in a LOS channel over flat ground with , we assume that power falls off at 40 dB/decade . Thus at d = 40 m, we would expect pathloss, dB. The average transmitted power of a pulse over the frequency range from 3.1 GHz to 5.8 GHz is given as
If synchronization integration is set to 64, this yields transmitted symbol energy of
If Noise power spectral density is assumed to be dBm·s, then received symbol energy to power spectral density is given:
The radios use BPSK modulation, hence the symbol error rate, , is given as
For a preamble length n = 443 symbols, and if then the probability of dropping a packet is given by
(see ). Thus if synchronization integration is set to 64, path loss should not be a significant factor in packet loss rate. Experimental data shows that there is no significant correlation between distance and dropped packets and we conclude that dropped packets in an open channel at distances from 2 m to 40 m are primarily the result of fading.
The leading edge detection parameter for scan step size was set to or 0.162 cm which is about 23% of a pulse width. If a sample misses the leading edge on average by , then we would expect a mean bias of 0.08 cm for each of the LED operations, for a total mean bias of 0.162 cm. Multipath components can obscure the leading edge also contributing to range errors. Measurements revealed a mean round trip bias of 0.18 cm using these settings. The scan window size was evaluated at two values, 20 nanoseconds and 100 nanoseconds. Preliminary experiments showed that 20 nanoseconds is the minimal size that offers reliable performance in a wide range of conditions and has a delay of approximately 3.2 milliseconds for LED with . The 100 nanoseconds window size was selected for the urban environment, where delay spread can be a very significant factor. The delay measured for the 100 nanoseconds window with is 6.7 milliseconds. The total constant delay for range measurements at 20 nanoseconds and 100 nanoseconds scan window is 20.01 milliseconds and 27.10 milliseconds, respectively. Assuming a successful range measurement, we expect range measurement errors to result primarily from LED sampling error and multipath fading. Experiments verify that the mean and variance of range errors are not correlated with distance.
Mean and standard deviation (m) for range errors for measured and theoretical model for open and urban channel and scan window = 20 nanoseconds and 100 nanoseconds.
Range error, mean and standard deviation ( )
Scan window (ns)
Measured Std Dev
Theoretical Std Dev
% Outliers, % dropped packets, and total lost ranges for open and urban channel with 20 nanoseconds and 100 nanoseconds scan window.
Scan window (ns)
% Range outliers
% Dropped packets
% Lost ranges
3.1. Position Error in a Static Environment
From two ranges, and we can determine the angle and position given by , as described in Section 2. Let represent the range error distribution and a sample from the distribution. Then the error in the angle θ 2 can be represented as
Then the measured position is given as
For two coordinates, and with measured values , we can determine the error in position measurement as
Letting represent the mean value of ϕ i , the RMS of the position error at time is given by
Mean update delay for open and urban channel with 20 nanoseconds and 100 nanoseconds scan window.
Scan window (ns)
Mean update delay (ms)
Std Dev update delay (ms)
In this section we analyze position accuracy for a target in motion based on the experimental data collection. For a mobile target, additional uncertainty in the position measurement is generated due to the finite duration of the range measurements and the interval between position updates. We propose a mobility model based on platform specifications and determine the resulting errors in position measurement due to mobility.
4.1. Mobility Model
The mobility model aims at examining position error in the worst case situations where the operator and platform are approaching each other with a mean relative velocity equal to the maximum allowable relative velocities specified in Table 1. Flat terrain is assumed and position is measured in two coordinates. A random, zero-mean, piecewise constant acceleration is applied in both coordinate directions with a standard deviation of 1 m/s . The relative distance between the platform and operator varies from 3 m to 40 m. A total of five crew members are tracked so that the update of the each position is delayed by four additional position measurements. Recall that each pair of coordinate radios is assigned a zone in such a way that errors due to geometry are balanced between zones. Because of this, analysis is restricted to motion in front of the vehicle and we assume that motion is confined to and , shown in Figure 5.
The position update rate at any time varies randomly, depending on the occurrence of dropped range packets. Let represent the time of the k th position update, where . If we assume that random acceleration is piecewise constant between the sampling intervals then the position of the target at time in each coordinate is described by
where and is the mean velocity over the interval .
Since , , and , we have
As is constant during the interval from to , then and for we have the position and velocity given by
For a position measurement that occurs at the true position for each dimension is given by
The mobility model for the discrete two dimensional case is given by
Initial values for mobility model for four test cases.
Delays during the range measurement that cause additional range measurement uncertainty for 20 nanoseconds and 100 nanoseconds scan windows in a mobile channel.
Scan window size
Given a continuously moving target, the position error at any time, , will be affected by the interval between position measurements. The position update interval depends on the time taken to measure range, the total number of range attempts needed to determine a position, and the number of targets being tracked. In our system, at least two range attempts are required, possibly more in the event of dropped range packets or outliers. If we assume that targets are updated in a round robin manner, then we have the position update date delay for the th position for the th target as
where is the constant time to complete a range measurement for a given radio setting; is the total number of ranges measurements for the th target on the th iteration; is the total number of targets, assumed to be constant.
From (15) we are given the position of the target at any time . If the most recent measured position is given by , then at some time , where the error between the true position and the last measured position 2 is given by
Letting represent the mean value of , the RMS of the total error at time is given by
Note that at .
The most significant impact to position errors in the mobile channel includes affects caused by GDOP and errors caused by delay during ranging and position updates. Errors due to GDOP are limited by creating zones around the vehicles that balance GDOP between zones and attempt to maximize . Errors due to ranging and position update delays can be reduced by using the shorter 20 nanoseconds scan window option. This option is shown to be less effective than the 100 nanoseconds scan window in a dense multipath environment with respect to percentage of range outliers. However the problem with range outliers is seen to be relatively insignificant when compared to percentage of dropped packets and additional errors caused by the delays required for the larger scan window. The modeling strongly suggests that a smaller scan window that results in a shorter range measurement duration is more effective than a larger scan window, even in a dense multipath environment.
At closer distances (e.g., less than 10 m) and lower speeds (e.g., less than 2.2 m/s) where GDOP and delays are not as significant, the position accuracy is less than 0.5 m for 90% of cases using the 20 nanoseconds scan window. At greater distances and higher velocities, the combined effects of GDOP and delays cause position measurement errors result in mean errors exceeding 2.5 m for the 20 nanoseconds case and 3 m for the 100 nanoseconds case. The primary concern for the positioning system is safety, and a recommendation has been made to alter the accuracy requirement to vary with distance and speed. Consider that platform's relative velocity is constrained based on distance from the operators for safety reasons. Using similar reasoning, we can have very stringent accuracy constraints when the platform is close to an operator, but these constrains can be relaxed as the platform get further away from the operators.
This effort presents the characterization of range and position error measurement results which serve as a foundation upon which this system can be matured and remaining challenges can be addressed. Currently, we are analyzing prediction and estimation capability that will yield improved accuracy over position measurements. An important consideration for development of an estimation technique includes the computational delay associated with a particular approach. Embedded systems tend to be power and cost constrained, limiting the amount of available processing resources. It will be important to contrast the performance of simple approaches with low computational overhead (e.g., variations of the alpha-beta filter) as well as more sophisticated approaches that yield more accurate estimates (e.g., particle filters) with realistic limitations on CPU performance. Another important aspect for this evolving capability is the study of more complex channel environments such as obstructed channels and transitions between different types of channels such as urban, forest, snow, and foliage.
This effort was supported by the Convoy Active Safety Technologies Program.
- Ni J, Barton R: Design and performance analysis of a UWB tracking system for space applications. Proceedings of the IEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics, April 2005 31-34.Google Scholar
- Ni J, Arndt D, Ngo P, Phan C, Gross J: Ultra-wideband two-cluster tracking system design with angle of arrival algorithm. Proceedings of the IEEE National Radar Conference, April 2006 1-6.Google Scholar
- Schwarz V, Huber A, Tüchler M: Accuracy of a commercial UWB 3D location/tracking system and its impact on LT application scenarios. Proceedings of the IEEE International Conference on Ultra-Wideband (ICU '05), September 2005 599-603.Google Scholar
- Luediger H, Guirao MDP, Cassioli D, Stenzel E: Overview of an UWB system for LDR-L/T in industrial and logistics scenarios. Proceedings of the 16th IST Mobile and Wireless Communications Summit, July 2007, Budapest, Hungary 1-6.Google Scholar
- Riehle TH, Lichter P, Giudice NA: An indoor navigation system to support the visually impaired. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '08), August 2008 4435-4438.Google Scholar
- Mahfouz MR, Zhang C, Merkl BC, Kuhn MJ, Fathy AE: Investigation of high-accuracy indoor 3-D positioning using UWB technology. IEEE Transactions on Microwave Theory and Techniques 2008, 56(6):1316-1330.View ArticleGoogle Scholar
- Gremigni O, Porcino D: Field trials of an accurate UWB ranging system. Proceedings of the IET Ultra Wideband Systems Seminar, April 2006, London, UK (11371):169-175.Google Scholar
- Yu K, Oppermann I: Performance of UWB position estimation based on time-of-arrival measurements. Proceedings of the International Workshop on Ultra Wideband Systems, May 2004 400-404.Google Scholar
- Navarro M, Nájar M: TOA and DOA estimation for positioning and tracking in IR-UWB. Proceedings of the IEEE International Conference on Ultra-Wideband (ICUWB '07), September 2007, Singapore 574-579.Google Scholar
- Badorrey R, Hernandez Á, Chóliz J, Valdovinos A, Alastruey I: Evaluation of TOA estimation algorithms in UWB receivers. Proceedings of the 14th European Wireless Conference (EW '08), June 2008, Prague, Czech 1-6.Google Scholar
- Zhang G, Krishnan S, Chin F, Ko CC: UWB multicell indoor localization experiment system with adaptive TDOA combination. Proceedings of the 68th IEEE Vehicular Technology Conference, September 2008, Calgary, Canada 1-5.Google Scholar
- Youssef J, Denis B, Godin C, Lesecq S: Enhanced UWB indoor tracking through NLOS TOA biases estimation. Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '08), November-December 2008 1-5.Google Scholar
- Wann C-D, Yeh Y-J, Hsueh C-S: Hybrid TDOA/AOA indoor positioning and tracking using extended Kalman filters. Proceedings of the 63rd IEEE Vehicular Technology Conference, 2006 3: 1058-1062.Google Scholar
- Denis B, Ouvry L, Uguen B, Tchoffo-Talom F: Advanced Bayesian filtering techniques for UWB tracking systems in indoor environments. Proceedings of the IEEE International Conference on Ultra-Wideband (ICU '05), September 2005 6-10.Google Scholar
- Venkatesh S, Buehrer RM: Non-line-of-sight identification in ultra-wideband systems based on received signal statistics. IET Microwaves, Antennas and Propagation 2007, 1(6):1120-1130. 10.1049/iet-map:20060273View ArticleGoogle Scholar
- Fernandez-Madrigal JA, Cruz-Martin E, Gonzalez J, Galindo C, Blanco JL: Application of UWB and GPS technologies for vehicle localization in combined indoor-outdoor environments. Proceedings of the 9th International Symposium on Signal Processing and Its Applications (ISSPA '07), 2007, Sharjah, UAEGoogle Scholar
- Tüchler M, Schwarz V, Huber A: Location accuracy of an UWB localization system in a multi-path environment. Proceedings of the IEEE International Conference on Ultra-Wideband (ICU '05), September 2005, Zürich, Switzerland 414-419.Google Scholar
- Massatt P, Rudnick K: Geometric formulas for dilution of precision calculations. Journal of the Institute of Navigation 1989, 37(4):379-391.View ArticleGoogle Scholar
- Rappaport TS: Wireless Communications: Principles and Practice. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2001.MATHGoogle Scholar
- Stallings W: Data and Computer Communications. 8th edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2007.MATHGoogle Scholar
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