Multi-Domain Geolocalization via Direction-Finding and Probability Fusion

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Solution Overview

Problem

Conventional localization methods face challenges in dense signal environments with multiple frequency-agile transmitters of the same type, as they require signal parameters for accurate positioning.

Innovation Solution

A method using direction-finding measurements from spatially distributed receivers to generate a marginalized probability distribution of emitter positions, eliminating incompatible hypotheses to produce a result map without relying on signal parameters, employing Bayesian estimation theory and weighting factors for dynamic updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional localization algorithms use received signal parameters (transmission frequency, pulse width, pulse repetition rate) for fusion, then localization can be achieved in simple environments, but accuracy deteriorates significantly in dense signal environments with multiple frequency-agile transmitters of the same type

Engineering Contradiction:
Improvelocalization accuracyVSAvoidperformance in dense signal environments
Core Design Contradiction:
Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The invention extracts and utilizes only the arrival direction information from received signals, discarding other signal parameters such as transmission frequency, pulse width, and pulse repetition rate. This extraction of essential directional data while ignoring interfering parameters enables accurate localization in dense environments with multiple frequency-agile transmitters of the same type.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The localization process is segmented into independent direction-finding measurements at each receiver, with each receiver independently determining arrival directions without requiring signal parameter analysis. These segmented directional measurements are then combined through probabilistic fusion to achieve accurate emitter localization.

Inventive Principle:
Principle #1Segmentation

2Measurement precision

If signal parameters are required for accurate positioning, then localization precision can be maintained in simple environments, but the system becomes unable to handle multiple transmitters of the same type in dense environments

Engineering Contradiction:
Improvepositioning accuracyVSAvoidinterference from multiple transmitters of the same type
Core Design Contradiction:
Measurement precisionVSObject-generated harmful factors

Solution Approach 1:

The invention extracts only the arrival direction information from received signals, completely discarding other signal parameters such as transmission frequency, pulse width, and pulse repetition rate. This selective extraction eliminates the harmful effect of multiple transmitters of the same type while maintaining positioning accuracy through directional information alone.

Inventive Principle:
Principle #2Taking out (Extraction)

3Reliability

If direction-finding measurements are performed by multiple spatially distributed receivers, then localization robustness improves, but computational complexity increases due to combinatorial multiplication of partial maps

Engineering Contradiction:
Improvelocalization robustnessVSAvoidcomputational complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

Each receiver performs preliminary direction-finding measurements independently to determine arrival directions, creating partial probability maps before combination. This preliminary action at each receiver reduces the overall computational complexity by avoiding the need for complex joint processing of all receiver data simultaneously.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The invention transforms the localization problem from direct spatial coordinate estimation to probability space operations. By converting directional measurements into probability distributions and performing combinatorial multiplication in this transformed dimension, the system achieves robust multi-emitter localization while managing computational complexity through probabilistic fusion.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables accurate localization and determination of multiple frequency-agile transmitters in dense environments without requiring signal parameters, allowing for continuous refinement and real-time tracking of emitter positions.

Implementation Method 1

m spatially distributed receivers of observers, each receiving the electromagnetic signals

Methodology Applied
Scientific EffectElectromagnetic radiation detection: Electromagnetic Induction

Implementation Method 2

carrying out direction-finding measurements by each of the receivers with respect to at least a part of the emitters in order to obtain a respective direction-finding measurement result

Methodology Applied
Scientific EffectDirection finding:

Data Source

PatentEP4235203A1Method for a multi-domain geolocalization
Publication Date: 2023.08.30 AIRBUS DEFENCE & SPACE GMBH
  • EP4235203A1 patent drawingFigure 1
  • EP4235203A1 patent drawingFigure 2a~2c
  • EP4235203A1 patent drawingFigure 2d~4

AI summary

A method is proposed for determining positions of n target objects, each emitting electromagnetic signals, by m spatially distributed observers, the method comprising: carrying out direction-finding measurements by each of the m observers with respect to at least a part of the n targets, collecting the direction-finding measurements by one of the m observers or an external evaluation unit, ascertaining a geometric probability distribution from each of the direction-finding measurements in a partial map, combining the partial maps in order to generate a plurality of possible hypotheses, adding up the possible hypotheses with a respective weighting to form an overall map in order to obtain a marginalized probability distribution that takes all hypotheses into account, and eliminating, step-by-step, hypotheses that are incompatible with the marginalized probability distribution.