NOVATEL – Avviso di EOL per i sistemi GNSS/INS modello SPAN-CPT e SPAN-CPTNC NOVATEL

External Notice: NovAtel Announces End of Life for SPAN-CPT and SPAN-CPTNC

NovAtel Inc. is announcing the End of Life of the:

These products will be available for order until:
February 28, 2019 (or until inventory has been depleted)

Shipments may be scheduled for no later than:
May 31, 2019

NovAtel will continue to support and repair these products until:
May 31, 2022

For a complete list of affected part numbers and the associated replacement part numbers, please refer to the table below:


Discontinued Part Number Replacement Part Number


For a complete listing of NovAtel products at end of life, including the expiration of support and repair for those products, please refer to the discontinued products list on the NovAtel website at:

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dicembre 5, 2018 / by / in
OXTS – Indoor positioning with Locata case study: automated vehicle safety testing in indoor and GNSS-challenged environments


Indoor positioning with Locata case study: automated vehicle safety testing in indoor and GNSS-challenged environments


Designing and executing the necessary tests to develop, evaluate and compare advanced driver assistance systems (ADAS) and autonomous driving functions is posing an ever-growing dilemma to the automotive industry. The test setup must be repeatable and as independent as possible of time of day, weather conditions and test driver behaviour.

One such organisation facing up to the challenge is the Insurance Institute for Highway Safety (IIHS), and independent American body conducts which tests to assess how ADAS technology can prevent or lessen the severity of crashes. Several years ago IIHS identified a growing need to expand its test facilities while meeting the requirements for future testing, including all-weather operation and test automation.

In 2015 IIHS completed a $30 million expansion of its Vehicle Research Center (VRC), the centrepiece of which was a five-acre covered track designed to allow testing to continue in all weathers. An existing outdoor track was also expanded, bringing the total area of test track to 15 acres. Given the need to simulate crashes safely, accurately and repeatably, IIHS had also researched robotic equipment to automate some of the driving tasks.

While the covered track offered much needed all-weather testing capability, it introduced a challenge for the high-accuracy GNSS-INS measuring equipment that IIHS uses for testing. IIHS operates a multi-frequency GNSS base station with real-time corrections to provide the position, velocity and time (PVT) parameters that are required for testing and essential for operating robotic test equipment. However, tests on the covered track showed the equipment was not delivering the accuracy and repeatability needed, and it was concluded that the steel trusses of the covered track’s fabric roof were obstructing the GNSS signals. Finding an alternative positioning technology that could deliver the required positioning performance on both the open and covered tracks triggered a global technology search.


In 1997 Australian company Locata began developing an alternative to GNSS/GPS in order to overcome the limitations of satellite signal-based navigation systems while delivering centimetre-level accuracy. Key to Locata’s system is a time-synchronization capability, called TimeLoc, that allows its ground-based signal transmitters, known as LocataLites (LLs), to synchronize with each other to picosecond precision.

Locata’s hardware uses a number of receivers (top right) and transmitters (bottom right) mounted on a network of ground-based masts (left)

A network of LLs forms a GPS-like constellation that allows signal-based positioning within a serviced area. These networks can cover deep canyons, indoor facilities and other challenging environments where GPS struggles to operate and deliver centimetre-level accuracy with high reliability and guaranteed high repeatability. Locata-based commercial networks can operate as an alternative or an augmentation to GPS.

A Locata network was deployed at IIHS in 2013, with 16 LLs covering both the open and covered test tracks. The network was designed to meet two key requirements: firstly, accuracy of 10 cm or better at 95% confidence, and secondly, a very high degree of repeatability with a service availability (meeting the above accuracy requirement) in excess of 95% of the time. All Locata receivers were expected to time-synchronize with GPS time and use the same coordinate systems as GPS, so any GPS equipment tested, such as built-in navigation systems, can be compared easily.

The IIHS network was designed using Locata tools that simulate network performance for various LL locations and allow the definition of visible/usable areas for each LL. As both IIHS tracks need to be time-synchronized and then to be synchronized with GPS, a single LL was designated as the master for the network. However, the two tracks are separated by a significant height difference, so a chain of LLs was used to bring the TimeLoc from the open track to the lower, covered track. The site topology did not allow good height distribution of LLs, so the optional digital terrain model feature was utilised. The network infrastructure was built and is maintained by IIHS with support from Locata.

The Locata network at IIHS’s Vehicle Research Centre uses 16 LocataLite transmitters. LL1 was designated as the master for the network. Shading denotes HDOP quality in the serviced area

AB Dynamics

AB Dynamics is the world’s leading supplier of the driving robots used in automotive testing. Driving robots precisely and accurately control vehicle control inputs with a level of repeatability that vastly exceeds that of human test drivers. Historically, driving robots have been used for vehicle dynamics, durability and even crash testing, but when coupled with an accurate position measurement sensor they can execute centimetre-accurate path-following tasks. When developing ADAS the ability to accurately and precisely control vehicle position is key to recreating real-life scenarios.

AB Dynamics’ path-following software is an established and proven technology. Motion data is collected from an inertial measurement unit (IMU) at 100 Hz and fed back to the robot’s path-following controller. This controller employs a speed-dependent look-ahead algorithm that not only maintains the vehicle heading but also allows centimetre accurate path control.


OxTS specialises in the design and manufacture of GNSS-aided inertial navigation systems (GNSS/INS) for automotive testing. OxTS systems offer not only centimetre-level position accuracy but also movement data in all vehicle-axes at up to 250 Hz. OxTS’ RT-series of products are used by many of the world’s automotive manufacturers for everything from vehicle dynamics testing to multi-vehicle ADAS testing and validation.

Unlike standalone GNSS automotive systems, which are unable to output data during GNSS blackouts, or are affected by multipath errors, OxTS products are still able to compute position, orientation and velocity measurements because they are built around an inertial measurement unit (IMU) that does not rely on external signals.

However, systems that rely on inertial measurements only are also prone to accumulated position estimate errors, or drift, with time. In OxTS’ products these errors are mitigated by the GNSS input, and several other inputs can be used alongside the IMU platform to create a hybrid system where each technology addresses weaknesses in others. The end result is an accurate and reliable measurement system that works in challenging real-world conditions. This makes it particularly suitable for robotic applications where a sudden loss of position information, or sudden jumps in location or heading, can have serious implications.

When the OxTS system is configured to work with the Locata system, the built-in GNSS information is replaced by measurement input from the Locata receiver to produce accurate and reliable measurements while maintaining excellent position accuracy. Data is output via Ethernet and CAN to be used by other equipment, such as driving robots, or logged. Raw measurements are also logged internally to be downloaded for post-processing in order to test different scenarios or make other changes.

Automated platform demonstration

In October and November of 2017 IIHS, in partnership with Locata, OxTS and AB Dynamics, conducted a demonstration of its all-weather test facility. For this demonstration, an RT1003 GNSS-INS was used to receive PVT data from the LL receiver instead of the RT’s GNSS receiver. No specific configuration was needed to interface the RT with the Locata receiver and the setup could be run interchangeably with either a GNSS or a Locata receiver. Both the RT1003 and the Locata receiver were mounted on one of the vehicle’s rear seats.

Test vehicle’s manual controls remained accessible to the driver despite AB Dynamics’ driving robot. OxTS RT1003 GNSS-INS and Locata receiver were mounted on the rear seat (not shown)

AB Dynamics provided a flexible driving robot drop-in kit that was quickly installed without modifications to the vehicle. Even with the robot installed, the steering wheel, throttle and brakes remain accessible to the driver. At the heart of the driving robot is a dedicated real-time controller, which coordinates the steering and pedal robots and captures data at speeds of up to 1000 Hz.

The Locata antenna was fixed to a roof rack-mounted ground plane, approximately aligned with the centreline of the vehicle. A second Locata antenna was connected to a second Locata receiver to be used for post-processing accuracy analysis of the fixed baseline between the two antennas. This baseline was then used as the truth for Locata-only post-processing accuracy analysis.

Test Procedure

The test vehicle was driven in various driving patterns on both test tracks. Double lane changes (DLCs), conducted on both tracks, resemble the driving pattern needed for testing most collision-avoidance and lane-change features, while an S-curve driving pattern was used to simulate IIHS’s headlight evaluations.

Double lane changes, S-curves and laps were performed on IIHS’s open track. Double lane changes alone were carried out on the covered track

Analysis and results

Data analysis from two full days of testing focused on the accuracy and repeatability of the automated test setup as a complete system first and then Locata alone.

The foundation for a highly repeatable control system with positioning accuracy is a highly reliable Locata network that delivers repeatable DOPs and a number of ranging signals at any given track location. Repeatability of the numbers of LLs seen and the HDOPs were investigated for this purpose.

During the five repeats of the DLCs conducted at 45 km/h on the covered track the number of LLs seen remained constant at seven, as expected.

Double lane change data analysis showed that the number of visible LLs remained constant at seven (top). Bottom data trace shows HDOP count


For the 20 km/h lap scenario on the open track, the number of LLs varied between eight and nine, with the drop occurring at one end of the lap.

Variations in timings of the LL visibility drop on the open track were due to varying vehicle speed in turns; bottom data trace shows the HDOP count


Analysis of the 48 DLC repetitions from the covered track, carried out at a range of speeds from 10 to 45 km/h, revealed a high level of repeatability. In straight segments the control system was able to repeat all the runs with less than 4 cm of mean deviation. A mean deviation of 5 cm was seen in the turns due to the range of speeds and the increasing lateral acceleration at higher speeds. The standard deviation also followed the same pattern, remaining below 3 cm during the straight-line segments and increasing up to 5 cm during the turns. A standard deviation of less than 2.5 cm was seen throughout all parts of the scenario, demonstrating that the Locata/OxTS/AB Dynamics automated control system maintained a run-to-run mean deviation of 5 cm or better and a standard deviation of 2 cm during straight-line driving.

Top subplot shows best-fit path from data average of 48 DLC repetitions on the covered track; Middle subplot shows mean and standard deviation of cross-track error of all repetitions compared with best-fit path; bottom subplot shows mean and standard deviation of baseline error measured between the two Locarta antennas mounted on the vehicle


Locata baseline error from repetitions of all scenarios was then used to estimate a probability distribution function (PDF) to assess the Locata positioning system performance alone. This included close to 180,000 data points from around five hours of automated driving. This baseline error PDF gives a Locata positioning accuracy of 2.8 cm at 95% and 5.6 cm at 99.7%, which is far in excess of the IIHS requirement of 10 cm at 95%.

Probability distribution function of baseline error was far in excess of IIHS requirements


With the addition of a covered test track, the Insurance Institute for Highway Safety needed an accurate and repeatable measurement system. Locata was able to install a local network of LocataLites to form a GPS-like constellation of transmitters to provide centimetre-level accuracy. With the positioning solution from Locata and an inertial measurement solution from OxTS, AB Dynamics was able to demonstrate accurate and repeatable testing with an automated driving robot.


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novembre 28, 2018 / by / in
PPM TEST – Airbus in Germania opta per il sistema a fibra ottica Sentinel 3 RF

Airbus in Germany chooses Sentinel 3 RF over fibre system

PPM are pleased to announce the that another European Airbus site has chosen Sentinel 3. Airbus Defence and Spaceheadquarters in Ottobrunn, near Munich. have chosen to integrate the Sentinel 3 RF over fibre system to their data acquisition platform.

About Airbus Munich

Airbus are a partner in the Eurofighter Typhoon consortium which also includes BAe Systems and Leonardo. Other activities at the site include development and manufacture of Arian 5 rocket engines and production of solar panels for satellites. NASA’s James Webb Space Telescope was built in the Ottobrunn site which also houses some of Airbus’ support functions such as Airbus cyber security, information management and finance.

Saving time and improving accuracy.

Sentinel 3 is the worlds most advanced RF over fibre system for EMC testing. The system was designed for rapid and flexible deployment, from remote transmitters through to the controller and receiver chassis. Upgrading to a Sentinel 3 system is anticipated to save a significant amount of setup time. Furthermore, features such as automatic thermal temperature compensation and 0.25dB accuracy specification are designed to improve measurement results.

PPM-supported Integration

The Sentinel 3 system has been very well received by companies involved in the Eurofighter programme,” says Dr Martin Ryan – managing director of PPM. “We are very pleased to be supporting with another Airbus site with Sentinel 3. As always, our software team are ready to help with integration of Sentinel 3 into an existing data acquisition platform which typically might include current probes, E-field probes and RF amplifiers.

About PPM Test

PPM Test is a division of pulse power and measurement Ltd. which has been manufacturing RF over fibre systems since 1995. PPM have supplied RF over fibre to some of the world’s largest manufacturers of aircraft, military and civilian vehicles. The company develops and manufactures in Swindon, UK.


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novembre 23, 2018 / by / in
PPM TEST – Aircraft EMC testing (part 2)

HIRF Test Methodology

The final stage of aircraft clearance may involve limited illumination of the aircraft with threat level RF fields, typically over the range 10 kHz – 18 GHz.  As an alternative approach, with lower facility costs than for testing the total aircraft with threat simulators, the following methodology is being increasingly used

Measure the coupling of EM energy (transfer function)

Measure the coupling of EM energy (transfer function) into the interior of the aircraft over the total frequency band of all the environments by illuminating the aircraft with low level swept continuous wave (CW) radiated fields. These measurements are normally made in “free field” conditions. Where the transfer function is in terms of the external field to the internally induced cable bundle currents it is known as the Low Level Swept Current (LLSC) test (Figure 1) and when it is in terms of the external field to the internally induced fields it is known as the Low Level Swept Field (LLSF) test (Figure 2).

FIG.1 – Test Arrangement for the LLSC Test (nose antenna omitted)


FIG.2 – Test arrangement for the LLSF test

STEP 1 – Compute currents or internal fields from coupling measurements

Use suitable signal processing algorithms and compute from the coupling measurements the currents (100 MHz) at the equipment’s location, that would be induced by the HIRF environments on the wiring systems.

STEP 2 – Directly inject predicted threat currents or irradiate the equipment and its wiring

Directly inject the predicted threat currents on the wiring systems, or at higher frequencies (>400MHz), irradiate the equipment and its wiring being assessed with predicted threat fields. Appropriate modulation is applied to simulate emitter parameters. This testing can be applied at system rig level (alternatively termed the systems integration facility), providing the rig is an accurate representation of the aircraft system.

STEP 3 – Use current probes and broadband antennas

Cable bundle currents can be measured using small ferrite current transformers or probes.  The internal fields can be measured using small broadband antennas, such as the “Top Hat” biconical 1- 18 GHz receive antenna shown in Figure 2. Signals from the current probes/antennas are coupled back to the remote instrumentation using analogue FOLs to as high a frequency as possible.  Multi-channel FOLs, such as the PPM Sentinel 3 cover frequencies up to 3GHz. Above this frequency, cables are used with great care to ensure they don’t compromise the integrity of the airframe shielding being assessed.

The future of HIRF testing with FOLs

Future fibre optic technology will likely permit reliable FOLs to be developed to cover the full low level swept coupling range of 10kHz to 18GHz. This would dramatically improve the measurement dynamic range as such high frequency FOLs would remove the cable losses which become large above 1GHz due to the cable lengths involved. As an example, even low-loss microwave signal cables have loss figures of typically 1 dB/metre at 18GHz.


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novembre 22, 2018 / by / in
PPM TEST – Aircraft EMC testing

Avoiding instrumentation interference and signal losses at high frequencies

As part of an aircraft’s certification for flight, aircraft must demonstrate safe operation within a range of environmental conditions. This includes Electromagnetic Environmental Effects (E3) which, for civil aircraft, include High Intensity Radiated Fields (HIRF) and Lightning. Both HIRF and lightning testing involves either (i) high level whole aircraft testing or (ii) a hybrid test method of low level aircraft testing with high level equipment testing.

For either test method, fibre optic links (FOLs) are essential to prevent compromising the measurements. For example, during low level swept current and field measurements, FOLs are employed to provide signal gain and isolation from the generated EM environment, typically from 10kHz to 1GHz.

Prevent instrumentation influencing the aircraft’s transfer impedance

Instrumentation should not influence the aircraft’s transfer impedance whilst it is exposed to RF fields or simulated lightning – for example, currents that degrade the airframe shielding. Interconnecting cables that pass from the external environment to the internal aircraft environment will impact measurements, for example RF current can flow on the cable shields. It is important therefore that signal cables are not used to connect the external instrumentation to the field/current/voltage probes installed within the aircraft, whilst performing frequency or time domain measurements. This is particularly significant over the 10kHz to 1GHz frequency range, where cable coupling dominates the leakage mechanism into the aircraft under test.

Avoiding signal loss at high frequencies

The external instrumentation must be outside the measurement area which with large aircraft may lead to a separation distance of tens’s of metres. Signal loss then becomes a factor at higher frequencies. Such signal loss is also avoided by the use of a FOLs. A typical aircraft low level swept current measurement would require signal cables of 40 to 50 metres, introducing significant losses. FOLs are typically used with 100 or 200m link lengths, permitting signals to be coupled from the transducers installed on the aircraft to the measurement equipment for even large transporter/passenger aircraft, requiring fibre lengths of over 100m.


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novembre 20, 2018 / by / in
ELASTISENSE – Kit portatile per misurazioni con sensori di spostamento elastomerici

PTK Portable Test Kit with Elastomeric Displacement Sensors


The PTK enables easy yet precise displacement measurements of a system or tool, with up to two sensors, allowing system characterization and monitoring.

PTK is a Plug and Play system consisting of:
• 2x elastomeric displacement sensors
• 1x data acquisition unit
• 2x sensor cables
• 1x charging cable
• 1x USB cable to connect to a PC
• 1x User Interface software

Our displacement sensors are designed to detect and show micron-level displacements. By using two sensors the PTK can also provide relative displacement differences between two signals.

The displacement sensors are able to tolerate aggressive mechanical environments, and are therefore uniquely suited for complete cycle measurements of processes like stamping and deep drawing. Here the PTK allows characterization of a complete tool cycle, as well as detection of any abnormalities such as lack of parallelism, slugs and similar.

The User Interface screen shot below shows a PTK measurement of a stamping tool where slug and lack of parallelism of the tool were characterized.

0.5mm obstruction shown between 2nd and 3rd cycle as highlighted. The difference in peak height indicates a Lack of parallelism between the two measuring points of the sensors.

What can the PTK do for you?
The PTK is a cost competitive test instrument, for assessing the behaviour of your processes and tools, before investing in a full, integrated monitoring system in your production environment. The PTK also enables you to characterize the dynamics of your machines and tools at different operation conditions to provide data/driven optimisation.

The main benefits of PTK are:
• Easy to set up and use – Plug and Play
• Measure and monitor all 360 degrees of a cyclical process – e.g. strokes of a stamping or deep drawing tool
• Standard feature for saving measured data for post-processing and presentation
• Update frequency (max. 1000 samples/s)
• Option for detecting peaks and valleys in the measured signal

FIG.3 – Installation example of the PTK in a system

The software associated with PTK is for Windows based devices. The software displays sensor measurements in real time as well as saving the data to a .txt file. The data can therefore be imported into Excel for analysis, presentation etc.

FIG.4 – Data file example with 10 recordings/s

The graph below shows a real application example where a PTK was installed in a progressive stamping tool to measure tools characteristics in presence of an obstruction (slug). The example shows a change in tool stroke characteristic due to a 0.03mm slug.

The sensor technology
Being capacitive and highly elastic, the Electroactive Polymer (EAP) core is the highly accurate central element of our sensors. Being made solely from rubber also makes the sensor intrinsically tolerant of misalignments and high vibration levels.

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novembre 19, 2018 / by / in
LORD MICROSTRAIN – Espande la serie di nodi wireless per acquisizione dati con soluzioni OEM facili da integrare

LORD Sensing Microstrain Expands Remote Sensing Options with Easy to Implement Solutions

OEMS now have a quick option for embedding high quality wireless data into their applications

CARY, N.C. – LORD Sensing, MicroStrain — a global leader in sensing systems — has announced the addition of two wireless sensors that enable OEMs to remotely collect data from a range of sensor types.
“Building a wireless temperature data acquisition system can be difficult and time consuming,” said Chris Arnold, LORD Sensing Product Manager. “The TC-Link®-200-OEM ,the SG-Link®-200-OEM and the G-Link®-200-OEM sensors now allow customers to quickly and easily integrate wireless data acquisition into their product without worrying about signal conditioning and radio design.”
Already proven as a solution for electric vehicle fuel cell condition monitoring, the TC-Link®-200-OEM allows users to remotely collect data from a range of temperature sensor types including thermocouples, resistance thermometer, and thermistors. The technology was developed for use with common temperature probes, includes cold junction compensation for thermocouples and linearization of all temperature measurements. The nodes support high resolution, low noise data collection at rates up to 128Hz. The new sensor is miniature, light weight and designed to be easily embedded. Many of the same features found on other LORD wireless products are included on the TC-Link®-200-OEM.
Intended to be used with strain gauges (Wheatstone bridge input), SG-Link®-200-OEM was an early adopter for monitoring mining equipment. The sensor also allows users to remotely collect data from a range of sensor types, including strain gauges, pressure transducers and accelerometers. The node includes on-board shunt calibration for easy in-situ strain gauge calibration and supports high resolution, low noise data collection from one differential and one single-ended input channel at sample rates up to 1kHz. A digital input allows compatibility with a hall effect sensor for reporting RPM and total pulses, making the sensor ideal for many torque sensing applications.

The G-Link-200-OEM has an on-board triaxial accelerometer that allows high-resolution data acquisition with extremely low noise and drift. Additionally, derived vibration parameters allow for long-term monitoring of key performance indicators while maximizing battery life. Users can easily program nodes for continuous, periodic burst, or event-triggered sampling with the SensorConnect software. The optional web-based SensorCloud interface optimizes data aggregation, analysis, presentation, and alerts for sensor data from remote networks.

Both wireless OEM sensing solutions are compact in size and offer low power operation, making them well suited for battery powered applications.
novembre 2, 2018 / by / in
LUNITEK – Soluzione IoT per il Monitoraggio e il preallarme degli spostamenti

GEA SYSTEM – IoT solution for monitoring and early-warning of displacements

Based on:

  • Networks of GNSS L1 low-budget sensors
  • 3-D displacements with millimetre level repeatability


Application fields:

  • Landslides, subsidence, co-seismic deformations
  • Critical infrastructures
  • Deformations due to human
    • Activities: Excavations, injection/extraction of natural gas



  • GEASYSTEM nodes are composed of lightweight, compact, low energy consumption, single-frequency GNSS stations, power supply modules based on photovoltaic panels and 868 MHz wireless interfaces for the transmission of data
  • Receivers installed at a monitoring site form wireless mesh networks, transmitting in real-time GNSS raw observations and telemetry data to a gateway node, equipped with internet connectivity. Network data are collected at a server where a specific SW computes displacements with millimeter level repeatability
  • A dedicated dashboard (client application) allows the User to display data on a map and on graphics with time series of 3-D displacements and telemetry data
  • GEASYSTEM can be configured to send alert e-mail in case of system malfunctions or when specific User defined displacements thresholds are exceeded, thus allowing forecasting of possible dangerous events
  • Publication of displacements and telemetry data is made through REST web services allowing the integration of measurements in pre-existing and
    custom dashboards



Receiver: U-BLOX M8T
Antenna: MOBI MBGPS GNS-30-001; frequency range 1575.42±10 MHz; gain 30±3 dBi; SMA female connector; 3.3V (different options on request)


Type: 868 Mhz, link wireless 0.5W. TDMA proprietary transmission protocol
Antenna: Directional gain 13dBi or omni-directional gain 3dBi; SMA female connector; 3.3V (different options on request)
Range: 8 km LoS
Networking: Up to 14 nodes connected to a single gateway; mesh network; each node can be configured as node or gateway by the User


Photovoltaic panel: 20Wp (optional 50Wp)
Battery: 12V 12Ah (144Wh); charge from -15 to 50 Celsius
Autonomy: Potentially unlimited (considering 2 hours of sunlight per day – 20Wp panel); a fully charged battery at 20 Celsius can power up the station for more than 12 days in case of total dark
Power consumption: < 390mW on average (including antenna)



Protection: IP65
Dimensions: GNSS Unit 17x10x5.5 cm

GNSS Antenna 8×13 cm

Photovoltaic panel (20Wp): 34x50x2.4 cm

Wireless antenna (Yagi): +13dBi Whip. 63×18 cm




Programming language: C and C++
3rd party libraries: RTKLIB (v. 2.4.2, BSD-2 license); used for GNSS data reading and clocks syncronization
Processing: Proprietary double-differences batch least square estimator developed to reach maximum performances in term of displacements repeatability for static monitoring applications
Repeatability: From 1 to 3 mm RMS for 24 hours batch of data (according to distance and height difference between reference and rover and to satellite visibility and possible multipath)


Per maggiori informazioni contattaci:

settembre 7, 2018 / by / in
LUNITEK – ASCLEPIUS Software per il monitoraggio e la valutazione dei danni strutturali

ASCLEPIUS – Software per il monitoraggio e la valutazione dei danni strutturali

The software ASCLEPIUS is developed by:


Why should I monitor the health status of my building?

A real-time system monitoring the vibrational characteristics of a structure allows to detect potential damage at the earliest possible stage, bringing considerably benefits and increasing the safety of the occupant.

The reliability of the results is ensured both by the ad hoc sensors disposition and the calibrated calculation model, in which all the very own structural features of the building are taken into account.


Structural health monitoring: How does it work?

The use of sensors having high dinamyc range and low noise level, if combined with an advanced and dedicated software for signal processing and structural identification, guarantees the knowledge of the health status of the building in the strong motion phase as well as in its serviceability condition before and after a seismic event.

The software is in constant communication with the sensors, elaborating the signals in near-real-time and periodically extracting the modal parameters of the structure. These parameters can be considered like a «fingerprint» of the building and their changes over time can be used for an effective damage assessment. Each system is specifically calibrated on the related structure, in terms of both hardware (number of sensors and disposition) and software (implemented calculation model) in order to guarantee the reliability of the results.



Periodical dynamic identification from ambient vibration

During the regular serviceability condition of the structure, ambient vibration measurements are acquired several times a day. Once signals are sent to the monitoring software, a dynamic identification analysis is performed through automatic algorithms that use the Operational Modal Analysis (OMA) techniques in order to estimate natural frequencies (w), mode shapes (A) and damping (n) of the building under its operating conditions.

Figure 1. Cross-spectrum


Figure 2. System architecture


The methods for damage assessment that examine the changes in the vibration characteristics follow the basic idea that modal parameters depend on the physical properties of the structure. When a significant variation (Delta w) in the identified characteristics is detected, suggesting the occurrence of a potential damage in the structure, an automatic procedure is started to apply some damage identification methods by analysing the differences between the last modal characteristics and the last reference ones, assumed to represent the undamaged state of the structure.

Figure 3. Time-frequency analysis


Identified parameters can also be used to calibrate a FE model of the building through Model Updating techniques, to make the model as closely as possible to the reality.


Real-time strong motion monitoring

During the seismic event, the system acquires the signal recorded by sensors placed on the ground outside the building, that are designed to provide the main strong motion parameters for the considered earthquake, i.e. Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD), Arias Intensity (AI) and Significant Duration (SD).
At the same time, the software acquires the signals of the sensors located on the structure in order to calculate the real time displacements and related inter-story drift ratios (IDRs) that occurred during the seismic event. As the IDR is one of the parameters that is mostly related to the structural damage, the software can instantly give a warning about a potential damage occurrence, using specific capacity curves that correlates the IDR values with the expected level ofdamage in the structure.

Figure 4. Real time recorded channels


During a seismic event, the deformations induced in the building result in a reduction of its natural frequencies, due to the nonlinearities in the material response. If no structural damage has occurred this frequency drop tends to be recovered as soon as the earthquake ends.
The monitoring software uses time-variant AutoRegressive exogenous (ARX) models in order to carry out a time frequency analysis, identifying the frequency drop during the event. Monitoring the way in which the structure modifies its vibrational characteristics during a seismic event can be useful in the cumulate damage estimation.


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settembre 6, 2018 / by / in
LORD MICROSTRAIN – Reti di sensori wireless ad alta velocità per il Monitoraggio della deformazione delle strutture con efficienza energetica

Civil Structure Strain Monitoring with Power-Efficient, High-Speed Wireless Sensor Networks

J.H. Galbreath*, C.P. Townsend*, S.W. Mundell*, M.J. Hamel*,
B. Esser^, D. Huston, Ph.D.^, S.W. Arms*


The goal of this work is to develop and deploy a network of long life, low-cost wireless strain sensors to monitor civil structures. Previous work on RF sensor nodes which transmit periodically to a central receiver using time division multiple access (TDMA) has been reported [1]. With a low power sleep mode and 30 minute sampling interval, this system is estimated to operate for 5 years on one 3.6V Li-Ion AA battery.

However, deployment of the TDMA system on structures underscored the need for a high-speed wireless sensor network with user-triggered and event-triggered data streaming capabilities. To this end, this prompted the development of a second generation datalogging transceiver (75 Kbps , 2MB memory). These systems provide 1KHz, 3-channel data in continuous streaming mode or 2KHz, 3-channel data in data logging mode. The node preserves battery life by entering a low power sleep state. The node periodically awakens and listens for commands, or wakes via eventtriggered interrupt.

This high-speed system was installed on a heavily trafficked bridge in Vermont. Displacement sensors were attached to steel girders for static and dynamic strain measurement. Strain data were acquired via a wireless link. The wireless system is designed to remain long term on the bridge for interrogation under normal and controlled operating conditions.

Author Affiliations Key:
* MicroStrain, Inc., 310 Hurricane Lane, Suite 4, Williston, VT USA 05495 U.S.A.
e-mail: web site:
^ University of Vermont, Dept. Of Civil & Mechanical Engineering, Burlington, VT USA 05405


Aging civil infrastructure presents a need for an inexpensive, easy to install strain monitoring system [2]. Servicing and installation can form a large portion of the expense of such a system, since human access to civil structures often requires specialized equipment, liability insurance, and vehicle traffic control. Hard-wired systems increase this human access component, with the system distributed over a larger area on the structure. In contrast, a wireless strain monitoring system as shown in Figure 1, only requires access to desired measurement areas, reducing the human access component, and ultimately reducing the cost of implementation.


The primary research goals were divided into two categories. The first objective was to build a second-generation, high-speed wireless sensor network, based on a new datalogging transceiver platform. This platform adhered to the following design criteria: 1) high speed wireless sensor streaming 2) minimal, deterministic streaming latency 3) onboard, non-volatile, high capacity data logging 4) advanced sleep modes for long-term deployment 5) interfaces with quarter, half, and full bridge sensors 6) interfaces with magnetic mount DVRT displacement sensors.

Figure 1. Network of wireless strain sensors, deployed on bridge structure

The second objective was to design a magnetic-mount attachment method for a high-resolution DVRT (differential variable reluctance transducer). The DVRT magnetic mount design would adhere to the following design criteria: 1) simple to attach to steel structures 2) no harm to surface of target structure 3) rugged construction for extended field use 4) highly resistant to slipping or migration due to ambient loads, such as vibrations.


Data Logging Transceiver Architecture

The datalogging transceiver platform was developed from off-the-shelf components, relying on IC’s (integrated circuits) for miniaturization and cost reduction. Figure 2 is a block diagram schematic. The platform has 8 channels of analog input. Channels one through four feature amplified full-differential input, with software programmable gain and offset, with optional bridge excitation and completion for interfacing full, half, or quarter bridge sensors, such as strain gauges and load cells. Channels five through seven provide non-amplified pseudo-differential input, accepting analog voltages between 0 and 3 volts. The last channel is reserved for an onboard solid-state temperature sensor (TC1046, MicroChip Technologies, Chandler AZ). The platform also features three 12-bit digital to analog converters that enable wireless bi-directional control applications.

At the heart of the wireless sensor is a low power 8-bit micro-controller (PIC16F877, MicroChip Technologies) that collects sensor data via an 8-channel, 12- bit successive approximation A/D converter (MCP3208, MicroChip Technologies). This data can then be stored locally to an onboard 2MB flash memory chip (AT45DB41, Atmel Corporation, San Jose CA), or streamed wirelessly. If the latter collection method is chosen, a half-duplex, narrowband ASK transceiver (DR-3000-1 RF Monolithics, Dallas TX) sends the sensor data at 75 Kbps, over a 916.5 MHz carrier.

Figure 2. Datalogging transceiver block diagram


On the user/controller-end, a base-station with the same telemetry hardware receives the incoming data stream, and forwards the data to a PC via a standard RS- 232 serial port. Since the telemetry hardware is bi-directional, the base station can also send commands and data to the remote nodes. This allows the user to reconfigure the operational parameters of the nodes wirelessly, and trigger data collection sessions. The network topology was implemented as a single-hop, hierarchical model, capable of supporting hundreds of nodes per base-station. Combined with the welldefined behavior of the narrowband RF transceiver, the topology enabled a minimal and deterministic sensor streaming latency. The system latency was measured to be less than 2 ms by measuring the time between an A/D sample on the remote node, and reception of this data on the serial port of a PC.

Wireless sensor streaming occurs at a fixed rate of 75 Kbps, which allows for approximately 1700 data points per second, depending on the number of active channels. This relationship is shown in Table 1. Error detection is accomplished with a checksum byte for each sweep (defined as a frame containing a sample of each active channel). This error detection scheme was chosen due to its low overhead requirements, which allowed for greater data throughput.

Datalogging occurs at one of seven user-selected sample rates between 32 Hz and 2048 Hz. This is a sweep rate, covering all active channels, with a maximum aggregate bandwidth of 16,384 data points per seconds (when all eight channels are selected). This information is stored on a 2 megabyte, non-volatile flash memory chip. Since access to civil structures can be limited, battery life was one of the most important design considerations for the datalogging transceiver. RF communications often dominate energy consumption in wireless sensing applications [3], so it was
important to develop a communications protocol that minimizes radio usage on the remote node. Table 2 lists the power requirements of the wireless sensor. (All measurements are taken when interfaced to a single 1000-ohm full bridge strain gauge sensor).

It was also important to implement intelligent sleep states, since the nodes would remain on the bridge for large periods of time without user-interaction. The microcontroller on the remote node features a low power sleep mode that can be exited via a watch-dog timer, or an external interrupt. This enabled two low-power monitoring modes.


In the first mode, the microcontroller periodically awakes via a watchdog timer interrupt, turns on the telemetry hardware, and listens for a wake command from the base station. If it does not detect a wake command within 50msec, it returns to the same sleep mode. In the second mode, the microcontroller remains in the low power sleep mode until a rising external analog voltage triggers a hardware interrupt.

For the first anticipated test application (scheduled monitoring of strain on a bridge), the following design assumptions were used to estimate battery life. The bridge is to be inspected once a month, requiring that the remote nodes collect data for approximately two hours per session. In addition, the nodes would need to remain in standby mode for six hours during these twelve scheduled inspections. Based on these requirements, the nodes were equipped with a 3.6V, 19 amp-hour, lithium thionylchloride battery (D-Cell, Tadiran Lithium Batteries, Port Washington, NY) which should last an estimated three and a half years.

Magnetic Mount DVRT Strain Sensor

Following consultation with the bridge owners (Vermont Agency of Transportation) it was determined that no epoxy, welds or other permanent fixtures be used to attach the strain sensors. These constraints led to the development of rapidly attachable compliant sensor that does not alter or permanently bond to the steel. The design used a magnetically-attached ultra-high resolution DVRT displacement sensor. Variable reluctance transducers have been used successfully in both large scale [4] and high resolution strain measurements [5]. For this application, the transducer uses two
neodymium magnets for attachment. The magnets are shielded from the sensor by a 416 stainless steel cup. This cup concentrates the flux lines and increases the holding power to about 45 N.

Figure 3. Magnetic Mount DVRT Sensors

The chosen displacement transducer is a DVRT (NANO-DVRT, MicroStrain, Williston VT) and inline signal conditioner (DC-DEMOD, MicroStrain). This sensor is capable of linear displacement measurements with a resolution of 10 nanometers. As shown in Figure 3, each DVRT sensor required two magnetic mount blocks to attach to the target structure. The body of the sensor was mounted within one block and the ferrous core was mounted to the other block with a 100 mm long rod. The configuration results in a strain sensor with a gauge length of 100 mm. In this
configuration, one microstrain experienced over the 100 mm of substrate corresponded to 100 nanometers of core displacement. A machined aluminum spacing jig was used as an installation tool that guaranteed consistent spacing and angular alignment while allowing the core to slide freely, Figure 4.

For long-term stability, the attachments between the sensors and the steel beam must remain in permanent contact with no relative motion at the contact pads. The shear force resistance of the attachments was tested by applying a shear load with a calibrated “S” type load cell (Model 363, Revere Transducers, Tustin CA) and reading the DVRT output. When the applied shear load is high enough to move the attachment pad, the DVRT core begins to move within the sensor and is detected. Multiple types of changes in the interface between the metal attachment and the substrate were also tested, with the idea that a high friction interface would make the magnetic attachments hold even better. A diamond knurl pattern was machined directly onto the base of the stainless steel attachment. This knurl pattern significantly increased the coefficient of friction between the attachment and the substrate under test, and the reactive force was increased from approximately 9 Newtons to about 13 Newtons. This design was deemed to be the best candidate for use in field-deployable sensors.


Figure 4. Magnetic Mount DVRT Sensor Installation

Test Methods to compare DVRT with Conventional Foil Strain Gauge

Data were collected with both a magnetic mount DVRT sensor and a half bridge bonded foil strain gauge (1000 Ohms, Micro-Measurements, Raleigh, NC) mounted on a cantilevered steel beam (5.08cm x 0.64 cm, constant cross section). The beam was resonant in bending at ~20 Hz. To initiate a test, the beam was flexed and then released, creating a cyclic bending strain which exponentially decayed. The strain levels were approximately +/- 200 microstrain (peak-to-peak). Analog voltages from the DVRT signal conditioning and the strain gauge conditioner were recorded by a digital storage oscilloscope. Figure 5 is photograph of this test setup.

Figure 5. Magnetic Mount DVRT and Bonded Foil Strain Gauge on Steel Beam

Test Methods to Determine Temperature Coefficients

To determine the offset and span coefficients of the DVRT strain gauge, two test methods were employed. The first utilized an Invar block, to which the DVRT was affixed with magnetic mounts. Data were collected with the DVRT core removed, and with the core at two known physical displacements from the null position. The DVRT, its signal conditioning and its wireless digital transmission circuitry were placed inside an environmental chamber with programmable temperature controller (Thermotron). Data were collected for these conditions at 20, 10, and 0 deg Celsius. From these data, we were able to compute the system offset drift and system span coefficients independently of the linear expansion coefficients of the substrate to which the DVRT’s magnetic mounts were affixed.

To determine the effect that magnetic mounting of the DVRT on a steel substrate may bhave on the thermal coefficients, tests of the DVRT and strain gauge instrumented 4140 steel cantilever beam were also performed. The DVRT and instrumented cantilever beam were placed within the environmental chamber, and their requisite electronics were placed outside of the chamber. This strategy allowed us to focus on the DVRT’s contribution to thermal errors, by direct comparison to the bonded foil strain gauge.


Comparison to Foil Strain Gauge

The magnetic mount DVRT strain gauge’s performance was comparable to that of the conventional bonded foil strain gauge, as shown in Figure 6. The DVRT data are plotted as a function of the strain gauge data, and correlation coefficient (.9978) presented. Note that these data represents several complete cycles at the 20 Hz test frequency. The data indicate minimal hysteresis or phase lag, and the relationship of magnetic mount DVRT data to conventional bonded foil strain gauge data is very nearly linear.

Figure 6. Output of magnetic mount DVRT strain gauge vs. conventional bonded strain gauge

Determination of Temperature Coefficients

The results from the thermal tests of the magnetic mount DVRT, its signal conditioning and its wireless digital transmission circuitry system on an Invar block at various temperatures is plotted below in figure 7. The DVRT output is plotted on the vertical axis, in microstrain, by scaling its displacement output using its calibration coefficient and gauge length (100mm). The horizontal axis represents fixed Presented at 4th Int’l Workshop on Structural Health Monitoring Stanford University, Stanford CA, Sep 15-17, 2003 displacements measured using the DVRT’s output at room temperature. Slopes and offsets were calculated for the three temperatures tested. The temperature coefficient of span was -0.086%/ °C between 20 and 10 degrees Celsius and 0.482% between 10 and 0 degrees Celsius. The offset shifts were 18.7 microstrain per degree Celsius between 20 and 10 degrees Celsius and 14.6 microstrain per degree Celsius between 10 and 0 degrees Celsius. This offset shift may be largely attributed to the core material, which is comprised of hardened 316 stainless steel. The thermal expansion coefficients of this material ranges between 16 and 17.8 microstrain/ degree Celsius (Micro-Measurements, Raleigh, NC).

Figure 7. Output of DVRT vs. fixed strain values at various temperatures

The results from the thermal tests of the magnetic mount DVRT and strain gauge instrumented 4140 steel cantilever beam are presented below in figure 8. The DVRT output is plotted on the vertical axis, in microstrain, by scaling its displacement output using its calibration coefficient and gauge length (100mm). In bending an additional scale factor is applied to account for the DVRT’s distance from the neutral axis of the beam which is greater than the surface location of the bonded foil strain gauge. On the horizontal axis we plot the output of the bonded foil strain gauge. A shunt calibration was performed to convert its signals in to units of microstrain. The temperature coefficient of span was 0.0023%/ °C between 22 and 50 degrees Celsius and -0.05% between 22 and 0 degrees Celsius. The offset shifts were -0.76 microstrain per degree Celsius between 50 and 22 degrees Celsius and -1.15 microstrain per degree Celsius between 22 and 0 degrees Celsius.

These results indicate that the DVRT has a relatively low temperature coefficient of span compared to the DVRT with its requisite signal conditioning. Compensation for the signal conditioning span error is warranted. One strategy is to implement an automatic shunt calibration.

The results also indicate that the temperature coefficient of offset is much lower for the DVRT combined with a steel substrate as compared to the DVRT on the Invar substrate. The apparent decrease in offset temperature coefficient on steel may be largely attributed to the linear expansion coefficients of construction steel which is between 10.8 and 12 microstrain per degree C, and begins to approach that of the DVRT stainless steel core. Offset and span errors may be corrected in software by use of an on-board semiconductor temperature sensor (MicroChip model 1046, Chandler, AZ) and an appropriate software correction algorithm which may reside in the remote units microcontroller or may be applied in a post processing routine.

Figure 8. Output of magnetic mount DVRT vs. conventional strain gauge at various temperatures

Field Trials on a Civil Structures

The first field-trial involved placing several datalogging transceiver nodes and magnetic mount DVRT’s on a heavily-trafficked steel girder composite deck bridge spanning the LaPlatte River in Shelburne, VT. The magnetic mount sensors were placed on the bottom flange of the central beam, and also near the bottom of the beam web, as in Figure 3.

Figure 9. Strain data collected during the passage of two large trucks

Installation of each sensor required less than five minutes. The base station was connected to a laptop computer, located approximately 35m away from the sensor nodes. The noise floor measured with no vehicle traffic (+/- 1.5 microstrain) was slightly higher than expected. However, with heavy traffic on the bridge, the system successfully collected and transmitted data. Figure 6 shows data collected while two large trucks traversed the bridge, each traveling in opposite directions.


A wireless strain sensor that can be quickly installed, without damaging the target structure has been developed and demonstrated. It can be deployed for long periods of
time, with a collecting strain data at 2 kHz with a resolution of +/-1.5 microstrain. The first field trial was successful. The system is currently undergoing long-term testing to evaluate reliability, drift, and environmental resistance. Enhanced signal conditioners are under development which provides improved thermal stability under harsh operating conditions.

This material is based upon work supported by the National Science Foundation under Grant No. (0078617, 0110217).

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