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A Cognitive Receiver for Indoor Positioning - By : Maherizo Andrianarison, René Jr Landry, Mohamed Sahmoudi,

A Cognitive Receiver for Indoor Positioning


Maherizo Andrianarison
Maherizo Andrianarison Author profile
Maherizo Andrianarison is a PhD candidate in electrical engineering at the ÉTS. He holds a master’s degree in networks and telecommunications from the ENSEEIHT. He is working on GNSS signal processing in constrained environments.

René Jr Landry
René Jr Landry Author profile
René Jr Landry is a professor in the Electrical Engineering Department at ÉTS and the Director of LASSENA . His expertise includes embedded systems, navigation, and avionics.

Mohamed Sahmoudi
Mohamed Sahmoudi Author profile
Mohamed Sahmoudi is a professor in the Department of Electronics, Optronics and Signal Processing (DEOS) at the Institute for Higher Education in Aerospace Engineering (ISAE), Toulouse, France.

cognitive receiver

Header picture from the authors. Substance CC license applies.

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The first part of this article entitled An Indoor Positioning System using Cognitive Radio has presented the indoor Positioning system, its objectives and methodology. In this second part, we will see the architecture of the High Sensitivity Cognitive GNSS Receiver – HS-CGR, tests and the conclusion.

Architecture of the High-Sensitivity Cognitive GNSS Receiver (HS-CGR)

Designing a HS-CGR can be approached as using CR technology in GNSS receiver design. The purpose of adding cognitive capacity is to develop a system that is self-adaptive and autonomous. In order to do this, several different aspects must be taken into account:

  • Programmability: to ensure that the system’s behaviour can be modified by changing the design parameters;
  • Reconfigurability: to ensure that the system’s behaviour can be modified by changing the design itself;
  • Cognitive capacity: to ensure that the system’s behaviour can be adapted to suit the environment in which it operates (meaning that the system observes actions then learns from these actions) [1].

Figure 1 shows a diagram of the HS-CGR. The portion in grey (the lower section) illustrates a generic HS-GNSS receiver.

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Figure 1 Diagram of the proposed HS-CGR receiver

This figure illustrates the architecture of the proposed HS-CGR and the interactions between the classic HS-GNSS and the cognitive layer. Figure 2 clearly illustrates the two separate blocks: the physical HS-GNSS and the cognitive layer, also known as intelligent signal processing (ISP).

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Figure 2 Overall view of the proposed HS-CGR

The intelligence of the HS-CGR will depend on interactions between the various modules of the cognitive layer (cognitive decisions, signal analysis, signal synthetic generator, database and high-sensitivity techniques) with the various modules of the HS-GNSS (Figure 3).

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Figure 3 Detailed architecture of the proposed HS-CGR receiver

Signal Processing Technique

High-sensitivity techniques for GNSS receivers always starts with long integration. After an extended coherent integration, the non-coherent combination of correlation outputs makes it possible to increase the power of low-strength signals. Using differential correlation techniques was proposed.

For situations in which receiving satellite signals is challenging and signals are very weak, a recent approach called “collective detection” or “collaborative detection” has been proposed. This approach makes it possible to process signals from all satellites in view at the same time. The proper combination of correlation values from several satellites can reduce the level of C/No required from satellite signals that cannot be acquired separately, and collectively they can contribute to a positioning solution [2] (Figure 4).

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Figure 4 GNSS signal acquisition employing conventional (sequential) and vector (collective) detection [6]

With this approach, the search uses position/clock bias, unlike the conventional approach that uses code/Doppler estimation. The idea is to be able to calculate the position directly after the acquisition process, without having to rely on the tracking process (Figure 5).

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Figure 5 Principle of Collective Detection Metric Computation [6]

Test Scenarios

Figure 6 illustrates where the GPS L1 C/A signals were recorded in downtown Montreal (near ÉTS) for the test scenarios.

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Figure 6 Recording signals around the ÉTS area

Here is an example test scenario conducted at ÉTS. The reference receiver (Rx GNSS from LASSENA) sends data (ephemeris, pseudo-range, position, etc.) to the mobile receiver (USRP B210) to help the acquisition process in order to calculate its position.

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Figure 7 Architecture of Collective Detention Tests

Long integration is always best for increasing receiver sensitivity. Applying this technique is advisable, as it will help obtain a good correlation peak for each satellite before calculating the collective detection metrics.

Shown here are curves illustrating how, in post-processing, the correlation peak increased when the integration period was extended in the collective detection approach, even with weak signals received at 35 dB-Hz.

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Figure 8 Increase of the correlation peak when the integration period is extended.

We confirmed that extending the integration period results in a good correlation peak for collective detection metrics computation.

Conclusion

By carrying out this research project, we were able to make good use of the various GNSS satellites to create a multi-frequency/multi-constellation receiver. Using cognitive radio technology in GNSS receivers is sure to lead to new lines of research in this field. Similarly, using the new collective-detection approach to deal with the receiver sensitivity problems may pave the way to new applications, such as using other receivers to help a receiver that is experiencing problems while exchanging information on the ephemeris and their positions. All GNSS will be used by the new multi-constellation/multi-frequency cognitive receiver to ensure that sensitivity is good enough for new indoor applications, not currently possible with the GPS system on its own. New applications similar to Waze will appear. Currently, the broader geolocation market is focussed on intelligent transportation systems and location-based services, which are very promising. Resolving navigation problems associated with GNSS-denied environments is decidedly advantageous for both users and operators of satellite-guided navigation systems—particularly with the development of important mobile telephone technologies. This project helps reduce costs associated with the installation of additional positioning equipment in challenging environments.

Our work is unique in that we used collective detection to acquire multi-constellation and multi-frequency signals while using cognitive radio technology to make the receiver reconfigurable and more intelligent.

This thesis project is being conducted as part of a double-degree program at ÉTS and the Institut Supérieur de l’Aéronautique et de l’Espace (ISAE) in Toulouse, France.

Research Article

An article on the collective detection portion entitled “A New Method of Collective Acquisition of Multiple GNSS Satellite Signals in Challenging Environments” was accepted and will be presented at the Navitec Conference, held December 3 to 5, 2014 in Noordwijk, Netherlands. The full text will be available after the conference.

 

Maherizo Andrianarison

Author's profile

Maherizo Andrianarison is a PhD candidate in electrical engineering at the ÉTS. He holds a master’s degree in networks and telecommunications from the ENSEEIHT. He is working on GNSS signal processing in constrained environments.

Program : Electrical Engineering 

Research laboratories : LASSENA – Laboratory of Space Technologies, Embedded Systems, Navigation and Avionic 

Author profile

René Jr Landry

Author's profile

René Jr Landry is a professor in the Electrical Engineering Department at ÉTS and the Director of LASSENA . His expertise includes embedded systems, navigation, and avionics.

Program : Electrical Engineering 

Research laboratories : LACIME – Communications and Microelectronic Integration Laboratory  LASSENA – Laboratory of Space Technologies, Embedded Systems, Navigation and Avionic 

Author profile

Mohamed Sahmoudi

Author's profile

Mohamed Sahmoudi is a professor in the Department of Electronics, Optronics and Signal Processing (DEOS) at the Institute for Higher Education in Aerospace Engineering (ISAE), Toulouse, France.

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