Radar CoE Intern

Location: Singapore, Singapore

In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and space, our architects design innovative solutions that make our tomorrow's possible.

Thales established its presence in Singapore in 1973 to support the expansion of aerospace-related activities in the Asia-Pacific region. Throughout the last four decades, the company grew from strength to strength and is today involved in the primary businesses of Aerospace (including Air Traffic Management), Defence & Security, Ground Transportation and Digital Identity & Security. Thales today employs over 2,100 people in Singapore across all its business areas.

INTERNSHIP TOPIC: AI Orchestrator Algorithms for Collaborative Radars

Description:

The concept of collaborative radar has the potential to enhance surveillance and tracking capabilities offered by standalone radar systems. This approach improves accuracy and detection range, enabling better situational awareness.

In this internship, it is proposed to define collaborative scenarios, study orchestrator algorithms and identify potential performance gains using radar simulators with programmable dwells (transmitter and receiver beams, waveform):

• The study will start with a research and review of the current publications on this subject and the identification of orchestrator algorithms
• The next step will be to define collaborative scenarios and study the radar simulators
• Once the previous step is completed, the collaborative scenarios will be played on radar simulators, and performance gains will be evaluated using standard tracking Key Performance Indicators (KPIs)

Specific Responsibilities:

• Research and review of current publications on this subject

• Definition of collaborative scenarios

• Study of radar simulators

• Run collaborative scenarios with radar simulators and performance gains evaluation

Key Requirements:

• Keen interest in radar and AI

• Experience of building and training machine learning models is most welcomed

• Skills in scripting/programming (Python)

• Knowledge in signal and data processing would be an advantage

Expected Outcome:

• Collaborative Scenarios

• Python scripts and Machine Learning models

• Data analysis and evaluation report

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