²EECS Department, University of Tennessee, Knoxville, TN 37996, USA. Abstract â We have developed a novel UWB positioning system with millimeter range ...
A System Level Design Approach to UWB Localization Michael J. Kuhn¹, Cemin Zhang², Song Lin², Mohamed R. Mahfouz¹, Aly E. Fathy² ¹MABE Department, University of Tennessee, Knoxville, TN 37996, USA ²EECS Department, University of Tennessee, Knoxville, TN 37996, USA Abstract — We have developed a novel UWB positioning system with millimeter range 3-D accuracy. It is difficult to comprehensively simulate a system of this nature since many disparate components must be simulated including the analog front end, digital backend, UWB channel, 3-D tag and base station positions, etc. Only through a novel simulation framework where Agilent Advanced Design System (ADS) is used, combining its Analog/RF and Ptolemy digital simulators and also its Matlab cosimulation is it possible to accurately simulate the positioning system. Experimental results from our UWB positioning system are compared to simulated results to validate the accuracy of our simulation framework. Simulated results show how one stage of automatic gain control can significantly increase operating range while analog-to-digital converter specifications do not significantly affect our overall system performance due to the use of a sub-sampling mixer. Index Terms — Indoor positioning, UWB localization, simulation, ADS, automatic gain control.
I. INTRODUCTION UWB positioning systems have inherent advantages when applied to indoor environments, most notably their robustness to multipath interference. Clark et al. ran an experiment where five indoor positioning systems (signal strength, Wi-Fi, ultrasound, RF, and UWB) were tested in multiple locations inside an operating room at a hospital and compared for their performance in 3-D indoor localization [1]. The UWB system (Ubisense [2]) was the only system of the five to consistently achieve 3-D positioning accuracy of less than a meter. The most notable commercial UWB indoor localization systems are the Sapphire DART system from Multispectral Solutions, Inc. (MSSI) and the real-time localization system from Ubisense, shown in Table I [2-3]. As shown in Table I, the two systems share many common traits including operating range (greater than 50 m), frequency band of operation (5.8 – 7.2 GHz), 3-D positioning accuracy (10 – 15 cm), and localization methods TABLE I COMPARISON OF COMMERCIAL UWB INDOOR POSITIONING SYSTEMS Company
Freq (GHz)
Localization Accuracy Range Tags Method (cm) (m)
MSSI
5.9– 7.1
> 50
>100
TDOA
< 10
Ubisense
5.8 – 7.2
> 50
>1000
TDOA and AOA
< 15
978-1-4244-2804-5/09/$25.00 © 2009 IEEE
(time-difference-of-arrival or TDOA with or without angleof-arrival or AOA). The main limitation of current UWB systems is their achievable accuracy. We have designed and tested an UWB indoor positioning system which outperforms current commercial systems by an order of magnitude (i.e. mm-range 3-D accuracy) [4-5]. This system uses a unique noncoherent approach where only one channel is downconverted instead of an I/Q downconversion process. This is combined with a novel sub-sampling mixer to time expand the UWB signal and make it easier to sample with a conventional ADC (e.g. 100 MSPS). The final step in our implementation is a novel leading-edge detection algorithm which locates the rising edge of the incoming UWB pulse and is extremely robust to multipath interference [6-7]. The experimental results from this system show its mm-range accuracy to be intact even in a dynamic experiment where the transmitter is allowed to freely move about in 3-D [5]. This paper is organized as follows: Section II discusses in detail the simulation framework as it has been built off of the previous work in [7]; Section III includes simulation results with emphasis given to ADC design, how AGC can enhance the operating range of our system, and how the signal-to-noise ratio (SNR) of the incoming UWB pulse affects overall system accuracy. Section IV provides experimental results with a direct comparison to simulation results for validation of our simulation framework. Finally, Section V concludes. II. SIMULATION OVERVIEW The simulation framework we designed incorporates the analog components of our UWB transmitter, the analog and digital components of our UWB receiver, UWB channel effects including multipath as specified by the IEEE 802.15.4a channel model, and 3-D positions for the tag and base stations in our system. ADS is used in conjunction with Matlab to simulate the transmitter and receiver architectures outlined in Figs. 1 and 2. As shown in Fig. 1, a 300 ps Gaussian pulse, generated through a highly stable 10 MHz Vectron crystal, is modulated by an 8 GHz LO signal, which is generated from an Agilent source with low phase noise. The resulting signal is amplified and transmitted on an omni-directional monopole antenna. On the receiver side, a single element Vivaldi antenna receives the signal, then two stages of amplification lead to a mixer where the received signal is downconverted by another Agilent signal
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generator with low phase noise where LO1 and LO2 are offset in frequency, in this case by 130 MHz. Next, the signal goes through a low-pass filter and then through a sub-sampling mixer (described in [4]). It passes through an ADC (currently 105 MSPS, 10 bit), then through an FPGA where the leading-edge detection algorithm is run (described in [7]), and finally is sent to a computer. Fig. 2 shows the proposed setup of adding an AGC stage before the mixer in the receiver chain. The setup described in Figs. 1 and 2 is used both for simulation and to obtain experimental results. Figure 3 shows the simulated Gaussian pulse after being modulated by the 8 GHz carrier signal while Fig. 4 shows part of a pulse train after the transmitted pulse has been sent through the industrial LOS channel model described in [8]. Equation (1) shows the multipath signal generated by the IEEE 802.15.4a CM7 channel model while Equation (2) shows the corresponding pathloss model [8]. Next, the high level ADS schematic can be seen in Fig. 5. Highlighted in ,
exp
,
10
,
1
2
this figure are the Matlab source and sink (for channel model incorporation and FPGA simulation), the ADC, and the RF transient embedded component which provides accurate RF receiver modeling. Fig. 6a shows the final time extended simulated UWB pulse while Fig. 6b shows the corresponding pulse from our experimental system. Only through a comprehensive simulation framework where the analog components in the transmitter and receiver, including the sub-sampling mixer, and also the UWB channel are taken into account is it possible to obtain simulated results comparable to experimental results as shown in Fig. 6. These pulses are on the order of microseconds as opposed to nanoseconds for the original UWB pulse (system is using a time expansion factor of 1000 at the sub-sampling mixer). Only by setting up a simulation environment where the complete UWB positioning system can be simulated, including RF components, antennas, the UWB channel including multipath and pathloss, the ADC, and the FPGA, is it possible to get a true sense of system behavior and utilize this knowledge in designing better UWB positioning systems. More details on this setup will be presented at the conference. III. SIMULATION RESULTS As mentioned in [9], AGC and ADC selection play a critical role in the performance of UWB receivers. The simulation results are shown in Figs. 7-9. Fig. 7 shows how the AGC increases the SNR of the received pulse and how the SNR is instrumental in ensuring correct operation of the
leading-edge detection algorithm. For example, when the SNR drops below 6 dB, the leading-edge detection algorithm fails. Fig. 8 shows the quantization-noise-tosignal-ratio (QNSR) of the received UWB pulses versus distance for three different ADCs: 105 MSPS, 10 bit (currently used in our system), 400 MSPS, 12 bit (commercially available for relatively cheap), and 3 GSPS, 8 bit. The 3 GSPS, 8 bit ADC is currently top of the line technology and represents the upper limit of technology available for the ADC. As shown in Fig. 8, the QNSR of the 105 MSPS ADC is relatively high for all distances (e.g. -8 dB) whereas the 3 GSPS ADC has a low QNSR for short distances but it drastically increases as the Tx-Rx distance moves towards 4 m. Although there is significant variation among the ADCs in terms of QNSR, as shown in Fig. 9, the ADCs perform almost identically in terms of the most important parameter: accurate leading-edge detection of the incoming UWB pulse for a given SNR. All three ADCs fail as the SNR of the incoming pulse drops below 6 dB. This analysis shows how our system would benefit greatly from at least one stage of AGC while it depends little on ADC performance mainly because of the use of the sub-sampling mixer in our UWB receiver, which reduces the bandwidth of the received UWB signal to less than 50 MHz. The leading-edge detection algorithm provides a robustness to ADC limitations such as QNSR, which only adds to its importance in overall system performance. IV. EXPERIMENTAL RESULTS In order to validate our simulation setup, an experiment was run with our UWB positioning system where the transmitter was placed at a static point with four base stations positioned around the tag at known 3-D locations. An optical tracking system (Optotrak 3020, Northern Digital Inc.) was used to accurately measure tag and base station positions with 3-D accuracy of 0.3 mm and also to calibrate our UWB positioning system. The same setup was created with our simulation framework where the tag and base stations were placed at the same 3-D locations. Figure 10 shows experimental and measured results for the static experiment where the simulated root-mean-square error (RMSE) is 8.60 mm and the measured RMSE is 10.2 mm. More experimental results are included in [5] and will be presented at the conference. V. CONCLUSION A novel UWB simulation framework for comprehensive simulation and testing of our UWB indoor positioning system has been developed. This setup takes into account the effects of RF components, antennas, the UWB channel, the ADC, and the FPGA and corresponding leading-edge detection algorithm. Simulated received signals are similar to the ones produced by our experimental system. Also, an experiment was run in localizing the position of a 3-D static point with good agreement between simulated and
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measured results. Simulations show the addition of one stage of AGC can increase the system operating range to greater than 4 m while ADC specifications do not significantly affect system performance. REFERENCES [1] D. Clarke, A. Park, “Active-RFID system accuracy and its implications for clinical applications, IEEE Symp. on Computer-Based Med. Sys., Salt Lake City, USA, 2006. [2] Hardware Datasheet, Ubisense, Cambridge, UK, 2007, http://www.ubisense.net/media/pdf/Ubisense%20System%20 Overview%20V1.1.pdf. [3] Sapphire DART (RTLS) Product Data Sheet, Multispectral Solutions Inc., Germantown, MD, 2008, http://www.multispectral.com/pdf/Sapphire_DART.pdf. [4] M. Mahfouz, C. Zhang, B. Merkl, M. Kuhn, A. Fathy, “Investigation of high accuracy indoor 3-D positioning using UWB technology,” IEEE Trans Microwave Theory & Tech, 56(6), 2008, pp. 1316-1330. [5] C. Zhang, M. Kuhn, A. Fathy, M. Mahfouz, “Real-time noncoherent UWB positioning radar with millimeter range accuracy in a 3-D indoor environment,” accepted in IEEE International Microwave Symposium, 2009. [6] B. Merkl, “The future of the operating room: surgical preplanning and navigation using high accuracy ultra-
Hittite H565 Hittite H441 Amplifier Amplifier Single Element Vivaldi
Variable Gain Amplifier
wideband positioning and advanced bone measurement, Ph.D. Dissertation, The University of Tennessee, 2008. [7] M. Kuhn, C. Zhang, B. Merkl, et al., “High accuracy UWB localization in dense indoor environments,” IEEE Int Conf UWB, 2008, vol. 2, pp. 129-132. [8] A. Molisch, K. Balakrishnan, et al., “IEEE 802.15.4a channel model - final report,” Tech. Rep., Document IEEE 802.15040062-02-004a, 2005. [9] Y. Vanderperren, G. Leus, W. Dehaene, “An Approach for specifying the ADC and AGC requirements for UWB digital receivers,” Inst. Engr. Tech. Seminar, UWB Technologies and Applications, 2006, pp. 196-200. Hittite H553 Mixer Vectron VTC4 Clock
Hittite H462 Amplifier Monopole UWB Antenna
300ps Gaussian Pulse
PRF1 = 10 MHz
Hittite H441 Amplifier
Agilent E8257D Signal Generator LO1 = 8 GHz
Fig. 1. Block diagram of UWB transmitter with low phase noise 10 MHz clock and 8 GHz LO.
Hittite H220 Mixer
Agilent 83624B Signal
LPF DC-5 GHz Tektronix AFG3102 Signal
Sampling Mixer
PRF2 = 9.999 MHz
LO2 = 7.87 GHz ADC
FPGA Spartan 3
CPU
Fig. 2. Block diagram of UWB receiver with low phase noise LO, sampling mixer, and FPGA leading-edge detection. One configuration bypasses the AGC while the other configuration incorporates AGC into the receiver chain. 2.0
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Simulated UWB pulse train.
Source - Pulse Train with CM7 Pathloss and Multipath
UnPkCx_M U2 NumRows=1 NumCols=1000000
Receiver RF Transient Simulation N_Tones N1 TStep=TStep Frequency1=ADCFreq Power1=.01 W Phase1=0.0 AdditionalTones="" RandomPhase=No PhaseNoiseData="" PN_Type=Random PN
rx_sub_transient X4
CxToTimed C2 TStep=TStep FCarrier=1 Hz
MatlabCx_M M16 ScriptDirectory="C:/Research/mkuhn/ADS/transceiver1_prj/data" MatlabSetUp="global N1 outPulse; N1=1;load('Operating_Range_ADC_Analysis/CM7_Multipath/pulseTrainCM7_MP_250_mm.mat');" MatlabFunction="output#1 = testPT1(1,1:1000000);" MatlabWrapUp=
R R1 R=50 Ohm
ADC - 105 MSPS, 10 Bit TimedToFloat T1
RF Clk
A
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Var Eqn
VAR VAR1 TStep=10 psec ADCFreq=105 MHz ADCFreq2=400 MHz ADCFreq3=3 GHz ADCPeriod=20 ns ExpandedTStep=5 nsec PulseEnergy_joule=.535e-10 PulseWidth=180 psec
DF DF DefaultNumericStart=0 DefaultNumericStop=1000000 DefaultTimeStart=0 nsec DefaultTimeStop=10 usec
ADC_Timed A1 NBits=10 VRef=1 V INL=1.0 DNL=.5
DSampleRF D1 Ratio=500 Phase=0 AntiAliasingFilter=Off ExcessBW=0.5 Pack_M P4 NumRows=1 NumCols=2000
SpectrumAnalyzer Filter_Output_Spectrum_Peak6 SpectrumAnalyzer Filter_Output_Spectrum_Peak5
ADS Ptolemy Simulation SpectrumAnalyzer Filter_Output_Spectrum6
SpectrumAnalyzer Filter_Output_Spectrum5
Sink - FPGA Pulse Detection and Postprocessing
MatlabSink M9 ScriptDirectory="C:/Research/mkuhn/ADS/transceiver1_prj/data" MatlabSetUp="global N2 finalRxChainSigs; N2=1; finalRxChainSigs=[]; finalRxChainSigsQ=[];" MatlabFunction="saveSig(input#1);" MatlabWrapUp="finalRxChainSigsQ=finalRxChainSigs;save('finalRxChainSignalsQ.mat','finalRxChainSigsQ','N2')" NumberOfFirings=1
TimedSink Filter_Output_Time6 TimedSink Filter_Output_Time5
Fig. 5.
Agilent ADS Ptolemy simulation incorporating Matlab sources and sinks, a transient RF simulation, and a 105 MSPS, 10 bit ADC. -8
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Fig. 8. Simulated QNSR vs. distance for three ADCs: 105 MSPS, 400 MSPS, and 3 GSPS without AGC.
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Fig. 6. Time extended UWB pulse with multipath effects: a) simulation, b) experimental. 18
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Fig. 9. Simulated leading-edge detection 1-D error vs. SNR of received UWB pulse for three ADCs. Measurement Simulation
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Fig. 7. Simulated SNR of received UWB pulse versus Tx-Rx distance with and without AGC.
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Fig. 10. 3-D measured and simulated results for localizing a static point over 200 samples.
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