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Signal Source Positioning Based on Angle-Only Measurements in Passive Sensor Networks.

Yidi Chen¹, Linhai Wang², Shenghua Zhou²

¹State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao Street, Nanjing 210016, China.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This study presents a method for locating signal sources using passive sensors. It accurately associates sensor data to determine source positions, improving network performance with low computational cost.

Keywords:

accuracy analysis angle-only measurements data association passive sensor network signal localization

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Area of Science:

Signal processing
Sensor networks
Estimation theory

Background:

Passive sensors provide signal direction but not precise source location.
Accurate signal source localization requires associating data from multiple distributed passive sensors.
Multi-target data association is crucial for effective passive sensor network performance.

Purpose of the Study:

To develop a method for multi-target data association and signal localization in distributed passive sensor networks.
To estimate signal source positions using only angle-only measurements from passive sensors.
To analyze the impact of measurement accuracy on localization performance.

Main Methods:

Constructing multiple lines in a 3D scenario from angle-only measurements.
Classifying intersecting lines within a small volume as originating from the same source.
Estimating signal source position as the center of the intersecting volume.
Formulating statistical distributions for estimated source positions.
Implementing an association threshold based on minimum distance between lines.

Main Results:

The proposed method effectively associates passive observations for signal source localization.
Statistical distributions for estimated signal source positions are derived.
Numerical results demonstrate the impact of angle measurement and platform self-positioning accuracy.
The method achieves a high data association rate and positioning performance.
The approach offers a low computation cost.

Conclusions:

Distributed passive sensor networks can effectively estimate signal source positions.
The developed data association and localization method is robust and efficient.
Accurate angle and self-positioning measurements are critical for optimal performance.
This technique provides a valuable solution for passive sensing applications requiring precise localization.