SAW-Based RFID for NASA Ground Facilities and Planetary Habitats Patrick W. Fink/Richard Barton October 13, 2008

Contributors Phong Ngo G. D. Arndt, Ph.D. Julia Gross Chau Phan David Ni, Ph.D. John Dusl Kent Dekome Patrick Fink, Ph.D. Timothy Kennedy, Ph.D. Richard Barton, Ph.D. Greg Lin Emal Latifzai Robert Williams Yasser Haridi Kent Byerly, Ph.D. (Spatial Acuity) George Studor Robert Brocato (Sandia National Laboratories)

Courtesy RFSAW, Inc. Courtesy AirGATE Technologies NASA Use of 2.4 ISM SAW-Based RFID

SAW RFID – Sample Waveform

Initial Passive, Wireless Sensor Applications Major Challenges – For NASA sensor applications, often want many tags within interrogator field-of-view – Ranges > 100 feet are desired for many applications – Primary SAW material in use is very sensitive to temperature Good for temperature sensing, but makes other sensors more challenging Combination of these challenges prompted early applications based on: – Larger aperture interrogators + adaptive digital beamforming – Temperature sensing applications

Waveform Correlation to Determine Temperatures and IDs Measured response – Composite signal from multiple tags + noise Template response Waveform obtained by a priori measurement Analytically modified by range and temperature Correlation matrix formed from measured response and modified template responses Need to determine effect of non-zero cross-correlations on temperature and range accuracies Measured tag delay with temperature

Correlation simulation - background 40-bit Global SAW Tag (GST) tags from RFSAW, Inc. Simulation based on measured tag responses with simulated additive, white Gaussian noise; SNR = 20 dB Composite signal formed from summation of 11 tag responses – All tags have same energy (suspected worst case) – Tags assumed at different ranges and temperatures 2D correlation process – Straightforward entire waveform correlation Suitable for RFID/sensor interrogation in which all IDs are known a priori – 21 time scale increments representing 105 C range – 21 time delay increments representing 210 ns range

Correlation simulation – results (11 tags) Tag 1873 correlated with noise, only. Tag 1873 correlated with composite signal + noise. Tag 1873 – strongest correlation in population

Successive interference cancellation required Tag 1858 correlated with Tag 1873 present: false peaks. Tag 1858 correlated after subtracting estimate of Tag 1873: correct peak is identified. Tag 1858 – 2 nd strongest correlation in 11 tag population

Correlation Simulation Results Preliminary error statistics for 11-tag population: – Error in delay Mean: 0.53 ns Std. Dev.: 1.5 ns – Error in dilation Mean: °C Std. Dev.: 0.21 °C More simulations and tests required for statistical significance Need to determine error dependencies upon number of tags in population

Spatial Diversity to Isolate Sensor Clusters Collision avoidance plan: Correlations used to isolate tags within defined clusters Virtual (digital) beamforming limits collision from adjacent clusters

72-element Interrogation in Anechoic Chamber

Direction of Arrival in Anechoic Environment

Spatial diversity for collision avoidance [ Before – 2 tag responses – to be added] [ After – single tag response recovered ]

Operation in KSC Cryogenics Laboratory

Direction-of-Arrival: Two tags in clutter

Applications – Lunar Outpost Telemetry – Monitor tool exposure limits: temperature, shock, etc. – MMOD impact detection and location – Chemical and atmospheric sensing RFID Tag

Applications – Lunar Outpost Navigation – Lunar landing aids – Lunar “road signs” or “breadcrumbs” – Passive tag tracking RFID Tag Tracking

JSC Chamber A (Vacuum & Thermal Cycle) Application: Environmental Facility Wireless Sensors Adaptive interrogation of wireless temperature and pressure sensors Goals: T low = 20K; 100s of T-sensors; 10s of pressure sensors JSC Chamber A T-sensor configuration on inner shroud

Sensor coverage schemes for Chamber A Coverage from wall-based interrogators Coverage from floor-based interrogators

Next Wave of Passive, Wireless Sensor Applications Additional Challenges – Desire to integrate calibrated, passive commercial sensors with SAW devices Acceleration, acoustic emission sensors are primary targets – Still need many tags within interrogator field-of-view and long ranges – Sample rates significantly higher than our temperature applications: > 10 kHz, compared to 1-3 Hz

SNL Concept to Incorporate Commercial Sensors Sandia National Laboratory (SNL) concept: FET-loading of SAW IDT with passive sensor driving FET Interdigital Transducers (IDTs) Passive sensor types under evaluation: accelerometer, acoustic emission

Application Example: White Sands Test Facility

Application Example: Monitoring Cryogenic Fill Level SAW Tag Temperature Sensors