Radio Frequency T OF Distance Measurement for Low-Cost Wireless Sensor Localization

1. Radio Frequency TOF DistanceMeasurement for Low-CostWireless Sensor Localization Steven Lanzisera, David Zats and Kristofer S. J. Pister

2. Introduction 1. • Paper was published in 2010 • Localization is a hot topic • Creating location-aware sensor networks • Enabling mobile phones to host a lot of new applications • Requirements • Low-cost / low energy consumptionis crucial • If we require a certain accuracy – we have to deal with measurement errors

3. Introduction 2. • Methods of localization • Acustic (BeepBeep, we have seen it) • RF techniques (no GPS) • GPS • The paper proposes an RF solution • Low cost / narrowband • No time-syncronization required • No base-station required • Approaches the Cramér-Rao bound in noisy environment • Accuracy – only in the order of a few meters (!)

4. Abbreviations • RSS – Received Signal Strength • TOF – Time-of-flight • TWR – Two-way ranging • TWTT – Two-way Time Transfer • UWB – Ultra wide band • CMS – Code modulus synchronization • CRB – Cramér-Rao Bound • SNR – Signal-Noise Ratio • RMS – Root Mean Square • MSE – Minimum Squared Error • CDF – Cumulative Distribution • MSK – Minimum Shift Keying (FSK = Frequency ~)

5. Localization problem • Localization consists of two parts • Measure relationships between nodes • Using this information to determine position of nodes • Received Signal Strength • Well-studied method • Determines range based on signal strength • Very inaccurate

6. Alternatives • Using Ultra Wide Band ranging • UWB receivers are very complex and expensive • Narrowband solutions • They usually require time synchronization, that adds complexity again • A low cost – simple technology is needed with meter-level accuracy

7. The presented system • Two-way ranging system • CMS (Code modulus synchronization) • No time-sync required • Online measurement – Offline range extraction • Works well in noise-limited environment • Mitigates the effects of multipath propagation • Idea: take measurements on multiple frequencies • Approaches the Cramér-Rao bound • Room-level accuracy satisfied (~1-3m)

8. Cramér-Rao bound • Statistics – estimation theory • Expresses a theoretical lower bound on variance of estimators of a deterministic parameter • – unknown deterministic parameter – number of measurements – probability density function of – expected value • Cramér-Rao bound where

9. Test-implementation • Commercially available accessories • 2.4GHz radio – Frequency Shift Keying • IEEE 802.15.4 • Standard which specifies physical layer and media access control for low-rate wireless PANs • Zigbee, MiWi ... etc. • FPGA • Overall accuracy: 1m outdoor / 1-3m indoor

10. Localization method 1. • Multilateration • Determining the a 2D position with 3 reference nodes (reference nodes: fixed, known position) • More nodes – better accuracy

11. Localization method 2. • More reference nodes should be used than strictly necessary • The geometry of the ref. nodes is important • Collinear references do not work • This area is highly understood, the more important part is determining the position from erroneous measurements

12. Range estimation methods • RSS – constructive and destructive interference make it unsuitably inaccurate • Time-of-Flight methods • Speed of light = 299,792,458 m/s • 1 meter range accuracy = 3ns time resolution • Low-cost devices provide the same sampling resolution as their clock frequency ~50ns • Cost, complexity and terrestrial environment (in comparison with GPS) make TOF ranging unsuitable

13. Types of errors to consider • Clock synchronization • Noise • Errors of samping artifacts • Multipath channel effects

14. Clock synchronization • Usually a common time reference is required in TOF systems • TWTT – Two-way time transfer • mitigates the time offset, but not the frequency error (clock drift) – we have to deal with it! Tr,B Ts,B A B Ts,A Tr,A

15. Noise 1. • We consider white noise • The accuracy depends on two components: • Bandwidth (B) • Energy-to-noise ratio (Es/N0) • CRB: for most signals: ts – signal duration; SNR – Signal/noise ratio

16. Noise 2. • Increasing the bandwidth increases tsB • Larger bandwidth – improved noise perform. • CRB can be closely approached if: • Increasing the number of measurements improve the results in quadratic order • Conclusion: noise alone does not prevent 1m accuracy if bandwidth is over a few MHz

17. Noise 3. • Cramér-Rao bound as function of bandwidth • Basically, we increase power to increase Es/N0

18. Sampling error 1. • Range binning • Sampling rate: fs =2B • Estimating the time of arrival • The space is divided into bins with c / fs width • Sampling adds uniform uncertainity in each bin of : • This will be (43m)2 if B = 2Mhz and fs =1/B, BUT can be decreased to (1m)2 by making 1000 measurements

19. Sampling error 2. • Tracking, filtering, averaging can eliminate this error, but that is very unefficient • OR: Signal can be oversampled • Usually the sampling error dominates the overall error, and not the CRB (the noise) – unless the sampling is very fast

20. Sampling error 3. • (continued) • In real systems usually 15dB < Es/N0 < 30dB, and noise is not a problem • If we sample the signal above the Nyquist limit(fs >2B) the entire information is captured and smaller sampling error is achieveable • Interpolation can be done, but its complexity and power consumption is usually way out of the capabilities

21. Multipath effects 1. • The signal reaches the receiver via different paths – a path is called a channel • Impulse response of the channel: • i=0 represents the direct path • Received signal: (m(t) – transmitted signal)

22. Multipath effects 2. • Noise does not effect multipath performance • We consider the two-path case • For small periods, the , and are random variables, but they are freqency-independent over a given RF communication band • We consider them constant for small periods

23. Multipath effects 3. • A few MHz change in frequency dramatically effects the multipath environment • Because of interference (constructive/destructive) • Measured RSS (fixed transmitter/receiver)

24. Multipath effects 4. • Delay spread: time betweenfirst and last paths • Most of the signal bandwidth is observable if • Typical interpath delay, is more important • Indoors is usually between 5 and 10 ns • The estimate is blurred by the multipath effect • To resolve this problem we need B>100MHz, or at least B>1/ T R t Delay spread

25. Multipath effects 5. • Possible solutions to mitigate multipath effects: • Increase bandwidth • Estimating channel impulse response • Multipath bias reduction • The first two are well-studied • Using devices with larger bandwidth (UWB) is expensive and they consume to much power • The achieveable accuracy appears to be around 30m with the second method – not sufficient

26. The solution

27. Ranging error mitigation • The paper presents two new methods to mitigate all the errors • Code modulus synchonrization • Combats sampling effects and poor time syncronization • Frequency diverse range estimation • Improves range estimation accuracy

28. Code modulus synchronization 1. • CMS uses a periodic signal, to modulate an RF carrier, so large B*ts is possible (therefore noise is not a problem) • First shaded region: C transmits the code to D • The phases are offset, but D knows the length

29. Code modulus synchronization 2. • D samples and demodulates the signal, and stores it • At this point D has a local copy of the code, but it is shifted due to the clock phase offset • Now D sends back (two copies of) the code

30. Code modulus synchronization 3. • C receives the transmission of D, and records it, synchronized to its own local reference • The circular phase shift will be exactly undone this way because of the round-trip nature of the system • C computes the cross-correlation and the measured code-offset is the TOF

31. Code modulus synchronization 4. • The received code can be interpolated to improve resolution up to the noise limit • The system approaches the CRB even with a single measurement • Multiple measurements can be averaged – this helps achieving good noise-performance • Correlation and code-offset estimation can be done offline after the RT part has ended

32. CMS vs. TWTT 1. • CMS vs. TWTT • Only one node performs the calculation → better sampling performance BUT • The full processing gain of the system is not realized at second node → Noise penalty • This means, that the second transmission (D→C) contains noise from the first part (C→D)

33. CMS vs. TWTT 2. • Only one node performs the calculation → better sampling performance BUT • The full processing gain of the system is not realized at second node > Noise penalty • This means, that the second transmission (D>C) contains noise from the first part (C>D)

34. CMS vs. TWTT 3. • - number of code copies averaged • The last factor represents the noise penalty of CMS • For very low SNR, it is approximately ½ if no averageing is used ( = 1) • For moderate to large values of , there is almost zero penalty • Single measurement variance is also better • CMS is better to approach the CRB

35. Frequency diverse range estimation 1. • Mitigates the multipath effect • Takes measurements on several carrier frequences • The problem: • Signal comes via two paths: one direct, more reflected • There is a delay and phase difference between them • Only the phase depends on the actual value • IEEE 802.15.4 uses MSK, a version of FSK • When changes to , the signal from the second path have not arrived yet

36. Frequency diverse range estimation 2. • Simulation shows, that this can result in either positive or negative biases in range estimation • According to the figure, we should make measurements over the same channel, with different phase relationships – averaging the value will reduce the overall bias

37. Frequency diverse range estimation 3. • Because the phase differencedepends on the , they usedifferent carrier frequencies • The median of 16 estimateshad the best error performance,(compared to averaging): 80% below 3m error • The demonstration environment implements this method

38. Prototype 1. • Waldo device • 2.4GHz radio • DA interfaces • FPGA (Verilog) • Microcontroller (C) • Implementation • Bandwidth = 2MHz • Binary frequency shift keying: +/- 0.75MHz • Sampling: 5MHz digital demodulation • Demodulated data bandwidth limit: 2MHz with 16MHz sampling – randge bins of 19m

39. Prototype details • Ranging between node pairs • Coordination / acknowledgement • 16 measurements – median is used • Maintaining CMS (2-period-length code 32 times) • Non-RT processing offline (linear regression to estimate TOF)

40. Tests 1. • Better than 3m overall accuracy • Noise performance • Verification with cable and simulated noise • Work within a factor of 2 of the CRB Because of the limited dynamic range of the digital baseband processor

41. Tests 2. • Ranging demonstrations (compared to RSS) outdoor indoor

42. Tests 3. • Open area – 40×50m • Max distance: 70m • 4 static nodes • Simple MSE estimation • 80% of errors < 2m

43. Conclusion • CMS is a TWR method that approaches the CRB • Freq. diverse ranging estimation is a strategy that improves ranging in multipath environments • Overall accuracy: 1m outdoors, 1-3m indoors • Where Es/N0 is large, sampling error dominates the noise-induced error, but CMS avoids this • Easy implementation, low costs, no UWB device required

44. Thank you for you attention!