Established in 1981, the SCHOOL OF ELECTRICAL AND ELECTRONIC ENGINEERING (EEE) is one of the founding Schools of the Nanyang Technological University. Built on a culture of excellence, the School is renowned for its high academic standards and strong tradition in research. To support teaching and cutting-edge research, EEE is host to 11 research centres and more than 50 laboratories, which are well-equipped with modern facilities and state-of-the-art equipment. With about 200 faculty members and an enrolment of more than 4,000, of which about 1,300 are graduate students, it is one of the largest EEE schools in the world.
Many applications of unmanned systems require the acquisition and processing of data from a variety of sensors, e.g., radar, sonar, lidar, or video. Powerful algorithms for analyzing data from individual sensors are typically already available. However, it remains a challenge to merge information from multiple diverse types of sensors. Naively merging data from multiple sensors may often deteriorate the results in practice. Consequently, it is crucial to carefully design algorithms for data fusion. If done properly, the benefit of multiple diverse sensors can be fully exploited, leading to more accurate detection and tracking of targets.
We aim to develop efficient and scalable algorithms to consolidate data from multiple heterogeneous sensors. These algorithms will allow us to detect and track targets more accurately, as they make full use of the information available in the sensors at hand. The proposed algorithms are generic, in the sense that they can be applied to any kind of sensor signals; therefore, they are expected to find use in a wide range of applications.
More specifically, in this project we have the following aims:
• Algorithms for target detection using data from diverse sensors. The algorithms will be assessed in terms of specificity and sensitivity.
• Algorithms for tracking a single target using data from diverse sensors. The algorithms will be assessed in terms of tracking error (mean squared error), .
• Algorithms for tracking multiple targets using data from diverse sensors (TRL 6). The algorithms will be assessed in terms of tracking error (mean squared error) and association errors.
• Optimized and tested code and system, packaged into API. User document.
The research fellow will assist in conducting this research, writing journal publications, and preparing grant applications.
Master degree in Electrical Engineering, Industrial Engineering or Statistics
Required skills include signal processing, machine learning, data modelling, design of unmanned vehicles
Interested applicants please attach your full CV, with the names and contacts (including email addresses) of 3 character referees, and all relevant academic certificates to JDAUWELS@ntu.edu.sg
Electronic submission of application is highly encouraged.
Only shortlisted candidates will be notified for interview.
Application closes when the positions are filled.