# Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach

## TL;DR

This paper introduces a machine learning-based method for multi-target localization using data pattern recognition across multiple sensors, improving data association accuracy for unmanned system applications.

## Contribution

It presents a novel approach combining data pre-processing with machine learning algorithms like K-means and SVM for multi-sensor data association and target localization.

## Key findings

- Machine learning algorithms effectively group sensor data for localization
- Simulation results quantify and compare algorithm performance
- Enhanced accuracy in multi-target localization tasks

## Abstract

Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovative target location learning approach, where numerous machine learning approaches, including K-means clustering and supported vector machines (SVM), are used to learn the data pattern across a list of spatially distributed sensors. To enable the accurate data association from different sensors for accurate target localization, appropriate data pre-processing is essential, which is then followed by the application of different machine learning algorithms to appropriately group data from different sensors for the accurate localization of multiple targets. Through simulation examples, the performance of these machine learning algorithms is quantified and compared.