Showcase

Testbeds

  • GNSS-SDR – An open source Global Navigation Satellite Systems Software Defined Receiver

Demonstrator models

  • GNSS array-based receiver platform
  • 2.4 GHz antenna array transmitter
  • OpenInLocation Lab: Indoor location
  • Interactive Multiple Models in Inertial Sensors processing

 

GNSS-SDR – An open source Global Navigation Satellite Systems Software Defined Receiver

In the following years, up to ten new radionavigation signals will be available thanks to the deployment of the Galileo and Beidou systems and the upgrades planned for GPS (Global Positioning System) and GLONASS. This scenario opens a wide range of opportunities for research, where position accuracy and reliability are the final goals. GNSS-SDR is an open source software defined receiver aimed at the development of high-accuracy receivers for the masses (low-cost subcentimetric positioning).

Visit the GNSS-SDR Project: http://gnss-sdr.org/

Figure: GNSS-SDR block diagram and software interfaces.
Figure: GNSS-SDR confidence regions for both accuracy and precision.

 

Figure: Galileo signals acquisition and tracking

GNSS array-based receiver platform

Summary: The use GNSS technology in safety- and mission-critical services has raised the concern in recent times about possible GNSS Denial of Service (DoS) situations. In that sense, the GNSS vulnerability to interferences could become a real threat to the entire service integrity. In this work the interference mitigation capability of antenna arrays in GNSS signal acquisition process was addressed.

In this demonstrator we designed and implemented a flexible FPGA-based GNSS antenna-array receiver platform, which includes a multi-channel front-end and a DSP processor. The platform is  intended to be used as a reliable research tool for array experimentation, and its tightly coupled with software defined GNSS receivers. Below, a table summarizing the platform features. To sum up, the demonstrator allows for testing and validation of array-based GNSS algorithms in real-life GNSS scenarios.

TABLE

Goal: The goal of the setup was to evaluate array-based GNSS algorithms with applications to both interference and jamming signals rejection, as well as multipath mitigation. We implemented novel array-based acquisition and tracking algorithms that operate in real-time. The platform is  linked to the GNSS-SDR receiver (see Testbed description above) running on an external PC using a Gigabit Ethernet interface. This setup is extremely flexible and allows a large number of customizations, including interchangeability of signal sources, signal processing algorithms, interoperability with other systems and sensors, output formats, and the offering of interfaces to all the intermediate signals, parameters and variables.

Applications:

  • GNSS interferences and jamming signal detection and mitigation,

  • GNSS DoS protection,

  • High sensitivity GNSS receivers,

  • Robust time delivery and synchronization.

Results:

Figure: High level block diagram of the GNSS array receiver platform.

Figure: The GNSS array-based receiver is able to adjust the radiation pattern of the antenna in an electronic and adaptive manner. Then, pointing to GNSS satellites while nulling the directions-of-arrival of interfering sources can be accomplished by computing the appropriate beamweight vector.

Figure: Hardware details of the GNSS array receiver platform.

Figure: GNSS array prototype in our Anechoic Chamber simulating a GNSS interference scenario. Results show the interference protection offered by an array-based GNSS signal acquisition algorithm when compared to single antenna receivers.

2.4 GHz antenna array transmitter

Summary:

Goal: The main goal of the demonstrator is to provide a flexible platform to verify theoretical developments and experimental results in real-life scenarios.

Applications:

  • Communications systems,

  • Digital beamforming,

  • Smart antenna design,

  • Space-time modulations transmitter,

  • Orbital Angular Momentum transmitter.

Results:

Figure: Array transmitter high level block diagram.

 Figure: Array transmitter hardware setup in our anechoic chamber and radiation pattern measurements.

OpenInLocation Lab: Indoor location

Summary: It is commonly agreed that GNSS cannot be the unique technology for positioning, especially in indoor scenarios with no line of sight with the GNSS space vehicles. Global navigation systems need to be complemented with other local aids, such as inertial measurement units, external information provided by wireless networks, signals of opportunity or dedicated wireless infrastructure.

Goal: The mission of OpenInLocation Lab is to accelerate the development of cutting-edge indoor location solutions to be used worldwide by fostering collaboration and contributing with new ideas for indoor positioning, disseminating results and outreaching to new audiences where real users will shape development, integrating research and innovation processes through the co-creation, exploration, experimentation and evaluation of innovative ideas, scenarios, concepts and related technological artifacts in real life use cases.

Applications: Indoor location is widely recognized as a key enabling technology for a myriad of applications and services related to:

  • Logistics (asset and resource tracking).

  • Navigation and guidance.

  • Augmented reality.

  • Security applications: tracking and monitoring of unauthorized persons in secured areas.

  • Behavior and movement analytics.

  • Management: location and tracking of people and key resources.

  • Emergency services.

  • Search and rescue operations.

  • Automotive safety.

Results:

 

Interactive Multiple Models in Inertial Sensors processing

Summary: In this demonstrator, we experimented with an Inertial Measurement Unit (IMU) mounted on a toy car. The car was running on a closed track of around 1 meter in length. The objective was to track the car trajectory using only IMU measurements regardless the different manoeuvres the car might have. In-house car customization included IMU mounting and power supply.

Goal: The goal of the setup was to evaluate and gain insight on the use of the so-called Interacting Multiple Model (IMM) techniques. These methods are used in combination of a bank of Bayesian filters to account for a set of possible system dynamics (in this case, several car manoeuvres). The resulting IMM filter fuses the output of those filters for an overall state estimation as well as providing weights for the different models according to their likelihoods.

Applications: potential areas of interest include

  • Data fusion

  • Target tracking

  • Radar

  • Synchronization

  • Positioning and navigation

  • In general, problems involving filtering over multiple system models.

Results: