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Lecture #7: Localization of Nodes Part II - Case Studies

Lecture #7: Localization of Nodes Part II - Case Studies. Reading List for this Lecture. Lew Girod et. Al. “The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform,” UCLA CENS Tech Report.

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Lecture #7: Localization of Nodes Part II - Case Studies

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  1. Lecture #7: Localization of NodesPart II - Case Studies

  2. Reading List for this Lecture • Lew Girod et. Al. “The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform,” UCLA CENS Tech Report. • http://nesl.ee.ucla.edu/courses/ee202b/2006s/papers/L07/Girod06_SenSys.pdf • M. Maroti, P. Volgyesi, S. Dora, B. Kusý, A. Nadas, A. Ledeczi, G. Balogh, and K. Molnar, “Radio interferometric geolocation,” In Proceedings of the 3rd international Conference on Embedded Networked Sensor Systems (San Diego, California, USA, November 02 - 04, 2005). SenSys '05. • http://nesl.ee.ucla.edu/courses/ee202b/2006s/papers/L07/Maroti05_SenSys.pdf

  3. A Self Calibrating Acoustic Sensing Array

  4. Distributed Acoustic Sensing Array • Acorn Woodpecker Localization • Solution: • Surround trees with acoustic arrays • Arrays detect woodpecker(s) • Arrays estimate bearing to birds • “Cross-beam localization” to estimate number and location of birds • Key Problem: • Need 3D Array Position and Orientation • Design constraints • 3D: birds are in trees, 3D terrain • Spacing requirement: 20+ meters • Accuracy requirement • 2° bearing, 25 cm position • Resilient to environment • Ground foliage • Background noise • Weather conditions Ack: Girod, 2005

  5. Problem Statement Goal: Develop a self-calibrating system to support collaborative acoustic sensing applications, such as beam-forming and cross-beam localization. Target System: • Input: Node placement: • 3D, Outdoor, Foliage OK • 20m Inter-node spacing • Arrays are level • Output: Estimates: • XYZ Position ± 25cm • Orientation ± 2° Results in James Reserve • Accurate: Mean 3D Position Error: 50 cm • Precise: Std. Dev. of Node Position: 18 cm 70x50m Ack: Girod, 2005

  6. Why is this hard? Node 108 • Spacing / low node density requirement: • Requires high precision (10 μS) time synchronization • Acoustic range often > RF range  multi-hop timesync • Less range data available, larger impact of angular error • 3D positioning vs. 2D • Adds additional degree of freedom • Topologies tend to be flat i.e. poorly constrained Z • Orientation estimation • Adds additional degree of freedom • Accuracy of 2 degrees difficult with small baseline array • Noise and interference rejection • Ranging must acquire precise phase of first arrival • Foliage often obstructs LOS • Blocks/attenuates signal (esp. narrowband signals) • Increases odds of ranging errors Node 104 Ack: Girod, 2005

  7. Key System Attributes Node 108 • Works well outdoors, even in obstructed environments • Other systems tested at shorter range, no foliage • Works for relatively sparse nets: 20m spacing with foliage • Others work well only at high densities and larger scales • This is not always practical • Achieves better accuracy and precision, in 3D • The best competing system gives 50cm position error in 2D • Our system gives 9cm error in 2D, 50cm error in 3D with poorly constrained flat topology • Precise orientation estimation • Required to support cross-beam algorithms • Does not require magnetic compass • 3D DOA estimation • Angular constraints are critical to good 3D performance, especially given that most topologies are relatively flat • Multi-hop time synchronization with COTS 802.11 • Acoustic range >> RF range Node 104 Ack: Girod, 2005

  8. (-4,4,14) 0° 0° 14cm   90° (-4,-4,0) 8cm Acoustic Array Hardware • 4 condenser microphones, arranged in a square with one raised • 4 piezo “tweeter” emitters pointing outwards • Array mounts on a tripod or stake, wired to CPU box • Coordinate system defines angles relative to array Ack: Girod, 2005

  9. Emit Coded Signal Time-Synchronized Sampling Service Layer Ranging Layer Select Code Detection Algorithm Detection Algorithm <Range, , > <Code, Detect Time> Trigger <Code, Detect Time> Time Sync Control Traffic Multi-hop Network Layer <Range, , > <X, Y, Z, > Trigger Multilateration Layer Position Estimation Ack: Girod, 2005

  10. Noise Estimate Rate Skew Approx 1st Peak Phase SNR Code Filtering and Correlation Detection and Extraction DOA Estimation And Combining 1st Peak Phase Start Time , , V Signal Input 4 4 4 Range and DOA Estimation • Inputs: • The input signals from the microphones • The time the signal was emitted (used to select from input signal) • The PN code index used • Outputs • Peak phase (i.e. range) • The 3-D direction of arrival: , , and a scaling factor V • Signal to Noise Ratio (SNR) Ack: Girod, 2005

  11. Rate Skew Modulator FFT Code Start Time 2 KHz High Pass FFT FD Correlation Signal Input Extract 4 4 4 4 Filtering and Correlation Stage • Correlation • Generate reference signal from PN code index • Correlate against the incoming signal Ack: Girod, 2005

  12. Reference Observed Correlation • Signal detection via “matched filter” constructed from PN code • Observed signal S is convolved with the reference signal • Peaks in resulting “correlation function” correspond to arrivals • Earliest peak is most direct path Lag = Time of Flight Ack: Girod, 2005

  13. 4 6 SNR 1st Peak Phase Peak Detector TD Correlation TD Correlation Combiner DOA Estimator Max , , V DOA Estimation • 6-way cross-correlation of correlations  DOA Estimator • Filtered signals from each pair of microphones are correlated • Offset of maximum correlation between pair (“lag”) recorded • DOA Estimator uses least squares to fit “lags” to array geometry • Key: Resilient to perturbations in microphone placement • DOA estimate used to recombine signals to improve SNR • Final peak detection yields range estimate Ack: Girod, 2005

  14. Node Localization Problem • Problem: • Given pair-wise range and DOA estimates • Estimate X,Y,Z locations and orientation Θ for each node • Solved using iterative non-linear least squares R,, Ack: Girod, 2005

  15. Ranging Accuracy 40 1% Error (cm) 95% CI 0 Range (m) 90 0 Ack: Girod, 2005

  16. Angular Accuracy 2 Error in Degrees 0 360 0 True Angle Ack: Girod, 2005

  17. System Test: Court of Sciences • 10 nodes placed at yellow dots • Yellow lines denote tall hedges • Ground truth measured as carefully as possible and arrays aligned to point west. • Z axis was difficult to measure; used data from Google Earth, which is measured to the nearest foot. N Ack: Girod, 2005

  18. Repeatability: Per-node XY mean & std-dev X cm Y cm Mean Std-dev: X=3.18, Y=3.85 Ack: Girod, 2005

  19. Z and Orientation mean and std-dev Mean Std-dev: 1.37 Mean Std-dev: 49.15 Ack: Girod, 2005

  20. James Reserve System Test • Deployed 10 nodes in forested area. • In many cases LOS was partially obstructed. • Ground truth measured using professional surveying equipment. • Nodes were aligned to point approximately west by compass. N Ack: Girod, 2005

  21. James Reserve per-node mean and std-dev Mean Std-dev: X=3.48, Y=3.78 Ack: Girod, 2005

  22. James Reserve Z & Orientation mean/std-dev 100 0 Mean Std-dev: 3.15 Mean Std-dev: 17.1 -100 Ack: Girod, 2005

  23. Summary • Acoustic ENSbox platform supports distributed acoustic sensing • Highly accurate positioning in challenging environments • XYZ Position ±20cm • Orientation ±2° • Nearly order of magnitude improvement upon prior work • 9 cm XY error vs. 50 cm (UIUC) • Supports XYZ+Θ estimation • achieved with • fewer nodes • lower densities • more difficult conditions. Ack: Girod, 2005

  24. Radio Inteferometric Positioning

  25. Ranging Techniques Ack: Maroti, 2005

  26. Large-scale RIPS Experiment Ack: Maroti, 2005

  27. Extreme Scaling Mote (XSM) Ack: Heemin Park, 2005

  28. Measurement Ack: Maroti, 2005

  29. Measurement Ack: Maroti, 2005

  30. Ranging Ack: Maroti, 2005

  31. Ranging Errors Ack: Maroti, 2005

  32. Tuning CC1000 Radio • Frequency band: 40 MHz - 460 MHz • Fine frequency resolution: 65 Hz • Maximum carrier frequency error: 2 KHz Ack: Heemin Park, 2005

  33. Frequency and Phase Estimation • 9 KHz sampling at ADC • On-line processing for 256 consecutive samples • Moving average filter Ack: Heemin Park, 2005

  34. Localization • In a network of n nodes • At most 3/2*(n – 2)(n – 3) independent interference measurements can be made • For two fixed nodes X and Y, • dXUYV : (n – 2)(n – 3) ranges • dXYUV : (n-2)C2 = ½*(n – 2)(n – 3) ranges • Number of unknowns • 2n – 3 (2D) • 3n – 6 (3D) • A solution is invariant under translations, rotations and reflection • At least 8 nodes in 3D are needed • 20 measurements > 18 unknowns Ack: Heemin Park, 2005

  35. Localization • No existing localization algorithms for dABCD ranging • Applied genetic algorithm with error metric of a solution s, Initial Population Populate new solutions from the best20% (Crossing over & mutations) Remove the worst 20% Evaluation Ack: Heemin Park, 2005

  36. Centralized Localization Ack: Maroti, 2005

  37. Design Tradeoffs Ack: Maroti, 2005

  38. Summary Ack: Maroti, 2005

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