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Research Roundup: Soft information for IOT positioning

Soft information for IOT positioning

A new system enables interconnected smart devices to cooperatively pinpoint their positions in noisy, GPS-denied environments. (Image: Christine Daniloff, MIT)

A new system enables interconnected smart devices to cooperatively pinpoint their positions in noisy, GPS-denied environments. (Image: Christine Daniloff, MIT)

The billions of interconnected devices and sensors embedded in other devices, vehicles and even humans that collectively constitute the much-heralded internet of things (IOT) collect and share data used in myriad applications. This requires them to know their location, which is a challenge in GPS-denied environments, such as most indoor locations, tunnels and urban canyons.

A new approach helps networks of smart devices cooperate to find and communicate their positions in such environments. This “localization of things” could be helpful in applications ranging from autonomous vehicles to asset tracking, from supply-chain monitoring to smart cities and real-time mapping.

Traditional network localization methods estimate a single value for each geospatial variable, such as the distance between two nodes. Therefore, accuracy drops sharply in environments where multipath, a limited view of the sky, and other problems severely degrade GNSS and wireless signals. A paper by researchers at four institutions outlines a system to capture location information even in these challenging environments by fusing positional data of various kinds as well as digital maps.

The new method fuses data from various sensing measurements — such as radio, optical and inertial signals — and analyzes features of each signal — including its power, angle of arrival, and time of flight. It uses machine-learning techniques to weigh this “soft information” — the researchers call it that because their method does not favor any single “hard” number — to create a probability distribution of distances, angles and other metrics.

It also exploits contextual information from digital maps, dynamic models and node profiles to verify what is possible. For example, two nodes could not be 20 meters apart if they are both in an area with a maximum dimension of 10 meters.

To reduce the complexity and size of the data that it must collect to function, the new method identifies the most and least useful aspects of the received waveforms for the purpose at hand on the basis of a “principal component analysis.”

In simulations of challenging scenarios, full of reflections and echoes, the new system’s performance significantly surpassed that of traditional ones and consistently approached the theoretical limit for localization accuracy, while the accuracy of traditional systems dropped dramatically.

Citation:Soft Information for Localization-of-Things” by A. Conti, S. Mazuelas, S. Bartoletti, W. Lindsey and M. Win, Sept. 9, 2019, Proceedings of the IEEE.

Principle of the spoofing detection and direction-finding procedure. (Source: IEEE paper authors)

Principle of the spoofing detection and direction-finding procedure. (Source: IEEE paper authors)

Algorithm helps civil aircraft fight spoofing

Evolution in civil aviation foresees a greater role for GNSS in onboard navigation systems as opposed to traditional terrestrial navigation aids. This will require improvements in managing the threat posed by RF interference.

Fortunately, the availability of more GNSS constellations and two carrier frequencies now enables GNSS equipment used aboard civil aircraft to not only detect and monitor spoofing, but also determine from which direction it is coming.

A recent paper details a procedure to do this. It consists of a detection module that employs an algorithm to identify which signals tracked by the receiver are counterfeit, if any, followed by a direction-finding module that implements an efficient direction-of-arrival (DOA) estimator. The procedure requires three GNSS antennas and the same number of receivers, time-synchronized with a common clock, plus a signal processor that implements the detection and DOA estimation algorithms. The paper presents the design of the chain of algorithms and their preliminary tests in a laboratory setup, with the simulation of several spoofing attacks, assumed realistic in a civil aviation scenario.

Citation:  “An Algorithm for Finding the Direction of Arrival of Counterfeit GNSS Signals on a Civil Aircraft” by G. Falco, M. Nicola, E. Falletti and M. Pini, presented on Sept. 20, 2019, at the ION GNSS+ conference in Miami, Florida.

Joint Galileo/GPS Project on the ISS

The European Space Agency (ESA) and NASA conducted a joint Galileo/GPS space receiver experiment aboard the International Space Station (ISS). The objectives of the project were to demonstrate the robustness of a combined Galileo/GPS waveform uploaded to NASA hardware already operating in the challenging space environment — the SCaN (Space Communications and Navigation) software defined radio (SDR) testbed (FPGA) — on-board the ISS.

The activities included the analysis of the Galileo/GPS signal and on-board position/velocity/time (PVT) performance, processing of the Galileo/GPS raw data (code and carrier phase) for precise orbit determination, and validation of the added value of a space-borne dual GNSS receiver compared to a single-system GNSS receiver operating under the same conditions. A recent paper provides a general overview of the experiment (called GARISS) and describes its design, test, validation, and operations. It also presents the various analyses conducted in the context of this project and the results obtained, with a focus on the (Precise) Orbit Determination results.

Citation: “The joint ESA/NASA Galileo/GPS Receiver onboard the ISS – The GARISS Project” by W. Enderle, E. Schönemann, F. Gini, M. Otten, P. Giordano, J. Miller, S. Sands, D. Chelmins, O. Pozzobon, presented on September 20, 2019, at the ION GNSS+ conference in Miami, FL.

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