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Bearing-based Target Localisation in Search and Rescue Scenarios

Giulia Michieletto, Nicola Mimmo, Roberto Naldi, Angelo Cenedese

TL;DR

This work manages a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation in search and rescue scenarios in which the technology is based on electromagnetic transceivers.

Abstract

This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions.

Bearing-based Target Localisation in Search and Rescue Scenarios

TL;DR

This work manages a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation in search and rescue scenarios in which the technology is based on electromagnetic transceivers.

Abstract

This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions.
Paper Structure (11 sections, 18 equations, 6 figures)

This paper contains 11 sections, 18 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Problem Statement and Proposed Solution
  3. Sensing Technology
  4. Problem Statement
  5. Proposed Solution
  6. Gradient Estimator
  7. TX position estimation
  8. Formation Controller
  9. Theoretical Working Principles of the Proposed Solution
  10. Simulation Results
  11. Conclusions and Further Developments

Figures (6)

  • Figure 1: Scheme of the proposed solution involving $n$ ARTVA's gradient estimators based on ES paradigm, a recursive LS centralized TX position estimator, and a combinatorial formation controller.
  • Figure 2: Behavior of the counters $t$ (white circles) and $\tau$ (black dots) which, every $N$ steps, are incremented and reset, respectively.
  • Figure 3: Formation controllers comparison: combined ES+LS (a) vs ES and LS only (b-c). The top row shows the trajectories for the RX agents, the bottom row reports the errors in the TX position estimation - solid green lines mark $\pm1[m]$ range.
  • Figure 4: Trend of ${\sigma(t,N)}$ depicted in terms of punctual values for each research step (red dots) and interpolated dynamics (blue line). In the convex combination \ref{['eq:bar_pc_plus']}, ${\sigma(t,N)}=1$ implies that the fleet position is updated according the ES output, while ${\sigma(t,N)} \rightarrow 0$ indicates the formation controller major reliance on LS position estimation.
  • Figure 5: Evolution of the distance $d(\bar{p}_c,p_T)$ between the formation centroid $\bar{p}_c$ and the (true) TX position $p_T$.
  • ...and 1 more figures

Theorems & Definitions (1)

  • Remark 1