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Interference Localisation Methods using Direct Position - - PowerPoint PPT Presentation

May 07, 2023 132 likes •346 views

Interference Localisation Methods using Direct Position Determination Concept Joon Wayn Cheong Andrew Dempster Introduction GNSS signals are A network of phased inherently weak array sensors tuned to the GNSS band can Spurious

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Joon Wayn Cheong Andrew Dempster

Interference Localisation Methods using Direct Position Determination Concept

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IGNSS 2018 - UNSW Sydney Australia – 7-9 February2018 | 2

  • GNSS signals are

inherently weak

  • Spurious

transmissions and intentional jammers in the GNSS band threatens safety critical applications that depends on GNSS

Introduction

  • A network of phased

array sensors tuned to the GNSS band can be used to detect jammers.

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  • Narrowband

– Strong jammer signal strength will affect receiver performance – Can be detected using AOA

  • Wideband

– Weak jammer signal strength is sufficient to affect receiver performance – Can be detected using TDOA and AOA

Jammer Characteristics

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  • AOA: Angle of Arrival utilising phased array

processing

  • TDOA: Time Difference of Arrival utilising cross

correlation

  • Geo-localisation of jammer

– AOA: Intersection of lines – TDOA: Intersection of hyperbolas

Introduction

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  • A signal processing

technique to directly localise the jammer in the position domain

  • Aims to combine signal

energy from all antenna elements in the network

  • Provides better position

resolution than conventional methods

Direct Position Determination (DPD)

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  • Most DPD approaches models narrowband signals

(e.g. DPD, LOST, LOST-FIND, HR-DPD)

  • Assumes wideband signal as a combination of

multiple narrowband channels

  • These DPD algorithms does not exploit good

cross-correlation properties of wideband signals

Existing DPD Approaches

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Taxonomy of DPD Methods

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  • Signal model:
  • Eigen-decomposition
  • Form noise subspace

TARGET 1/2

Correct eigendecomposition requires Q < M

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  • Cost function:
  • Gridded position domain search:

TARGET 2/2

(left) X‐Y, (middle) Y‐Z and (right) X‐Z domain plot of the test statistic (z‐axis) vs position space (x,y‐axis)

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  • Requires assumed knowledge of Q
  • Limited number of detectable sources
  • Lack sensitivity

– Does not fully utilise signal energy from all antenna elements within the array

Limitations of TARGET

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  • Global

Covariance Matrix

  • Modified Global

Covariance Matrix

Cross-correlation DPD (1/2)

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  • Eigen

decomposition and cost function

Cross-correlation DPD (2/2)

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TARGET ccDPD

Multiple Jammer

SNR = 0dB

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TARGET ccDPD

Heavy Background 8x GNSS Signals

SNR = ‐10dB

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TARGET ccDPD

Large Number of Sources (Ns = 12)

SNR = ‐10dB

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Multiple Jammer Heavy Background GNSS Signals Large number of sources

Performance Evaluation

  • 10
  • 5

5 10 5 10 15 20 25 30

SNR (dB) RMSE (m) SNR (dB) RMSE (m) SNR (dB) RMSE (m)

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TARGET ccDPD

Field Data Results

SNR = ‐10dB

East (m) North (m) East (m) North (m)

  • 420
  • 400
  • 380
  • 360
  • 340
  • 340
  • 320
  • 300
  • 280
  • 260
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  • Derived a taxonomy and compared

various DPD approaches

  • Proposed ccDPD method has superior

SNR sensitivity in comparison to recent methods

  • Proposed ccDPD method can localise

more sources than TARGET

Conclusion

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Questions?

Email: cjwayn@unsw.edu.au

Acknowledgement

  • ARC Linkage LP140100252
  • GPSat Systems Australia
  • Dr Ryan Thompson
  • Dr Graeme Hooper