Integrated Techniques for Interference Source Localisation in the - - PowerPoint PPT Presentation

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Integrated Techniques for Interference Source Localisation in the - - PowerPoint PPT Presentation

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Integrated Techniques for Interference Source Localisation in the GNSS band Joon Wayn Cheong Ediz Cetin Andrew Dempster Introduction GNSS signals are A network of sensors inherently weak tuned to the GNSS band can be used to

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Joon Wayn Cheong Ediz Cetin Andrew Dempster

Integrated Techniques for Interference Source Localisation in the GNSS band

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 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 sensors

tuned to the GNSS band can be used to detect the angle of arrival (AOA) and time difference of arrival (TDOA) of the jammer.

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 3

  • AOA: Uses phased

antenna arrays DSP

  • TDOA: Uses cross-

correlation method DSP

Introduction

  • Geo-localisation of jammer

– AOA: Intersection of lines – TDOA: Intersection of hyperbolas – Can we combine AOA and TDOA for Geo-localisation?

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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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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 5

Cramer Rao Bound: AOA

  • Most of the errors are

within 10-40m

  • Errors behave smoothly
  • utside the convex area

←

  • ⋮

⋮

  • Σ ∈
  • Σ
  • CRB

Jacobian

AOA Measurement Covariance

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 6

  • Most of the errors are

within 5-40m

  • Errors behave erratically

due to rank deficiency beyond the convex area bounded by the 3 nodes

Cramer Rao Bound: TDOA

←

  • ⋮

⋮

  • TDOA Measurement Covariance: Σ ∈
  • Σ

CRB:

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 7

CRB for AOA + TDOA Integration

  • Most of the errors

are within 2-30m

  • Rank deficient

regions significantly improved

  • Lowest CRLB

achieved at all points

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 8

Fair comparison between independent localisation and integrated localisation

  • 2500 -2000 -1500 -1000
  • 500

500 1000 1500 2000 2500

  • 2500
  • 2000
  • 1500
  • 1000
  • 500

500 1000 1500 1 2 3 In Convex of 2 SN In Coverage of all 3 SN In Convex

AOA STD: 0.5 degrees TDOA STD: 11m

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Improvement over AOA-only

  • Improvement

measured in percentage (%)

  • 2500 -2000 -1500 -1000
  • 500

500 1000 1500 2000 2500

  • 2500
  • 2000
  • 1500
  • 1000
  • 500

500 1000 1500 1 2 3 In Convex of 2 SN In Coverage of all 3 SN In Convex

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 10

Improvement over TDOA-only

  • Improvement

measured in percentage (%)

  • 2500 -2000 -1500 -1000
  • 500

500 1000 1500 2000 2500

  • 2500
  • 2000
  • 1500
  • 1000
  • 500

500 1000 1500 1 2 3 In Convex of 2 SN In Coverage of all 3 SN In Convex

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AOA + TDOA Fusion Architectures

Loose Integration Tight Integration

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Input: AOA measurements , ∀ ∈ 1, … , TDOA measurements ̂, ∀ ∈ 2, … , Sensor Node Positions , , ∀ ∈ 1, … , Source Guesstimate Position , AOA Noise Covariance Matrix Σ ∈ TDOA Noise Covariance Matrix Σ ∈ Output: , Estimated Emitter Position Initialise , ← , Compute TDOA-only solution with arguments: ̂, , , Σ Output stored as , Compute AOA-only solution with arguments: , , , Σ Output stored as , Compute Position Error Covariance Matrix for ,

  • ←
  • ←
  • ⋮

⋮

  • Σ ←

Σ

Loose Integration Algorithm

Compute Position Error Covariance Matrix for , ←

  • ⋮

⋮

  • Σ ←

Σ

  • Perform Loose Integration

Σ Σ Σ ←

  • ←

Σ Σ

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 13

Input: AOA measurements , ∀ ∈ 1, … , TDOA measurements ̂, ∀ ∈ 2, … , Sensor Node Positions , , ∀ ∈ 1, … , Source Guesstimate Position , AOA Noise Covariance Matrix Σ ∈ TDOA Noise Covariance Matrix Σ ∈ Output: , Estimated Emitter Position Initialise , ← , Compute TDOA-only solution with arguments: ̂, , , Σ Output stored as , Compute AOA-only solution with arguments: , , , Σ Output stored as , Compute Position Error Covariance Matrix for ,

  • ←
  • ←
  • ⋮

⋮

  • Σ ←

Σ

Loose Integration Algorithm

Compute Position Error Covariance Matrix for , ←

  • ⋮

⋮

  • Σ ←

Σ

  • Perform Loose Integration

Σ Σ Σ ←

  • ←

Σ Σ

  • The key to loose integration is the

computation of an accurate Position Error Covariance Matrix for AOA and TDOA systems.

  • requires an approximate position to be

provided

  • provides a “weighing” mechanism
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Effect of incorrect weighing matrix

Correct Weighing Incorrect Weighing

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Tight Integration Algorithm

Input: AOA measurements , ∀ ∈ 1, … , TDOA measurements ̂, ∀ ∈ 2, … , Sensor Node Positions , , ∀ ∈ 1, … , AOA Noise Covariance Matrix Σ ∈ TDOA Noise Covariance Matrix Σ ∈ Output: , Estimated Emitter Position Iterate: , ← ,

  • ←
  • ←
  • ⋮

⋮

  • ⋮

⋮

  • Δ

Δ ←

Σ Σ

Δ ⋮ Δ

  • ⋮
  • ← Δ

Δ

  • End
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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 16

  • AOA and TDOA Integration provides superior

performance under all circumstances

  • Existing attempts to combine AOA and TDOA

has been suboptimal due to incorrect “weighing” and/or use of a Loose Integration Architecture

  • 2 architectures has been proposed that can

be adapted to various existing platforms

  • Proposed algorithms of both architectures

approaches the Cramer Rao Lower Bound

Conclusion

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 17

  • GPSat Systems Australia
  • Ryan Thompson

Acknowledgement

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IGNSS 2016 - UNSW Sydney Australia – 6-8 December 2016 | 18

Questions?

Email: cjwayn@unsw.edu.au