Supervisors:
Principal supervisor: Dr. Limin Yu (XJTLU)
Co-supervisor: Prof. Baojiang Li (JITRI)
Co-supervisor: Prof. Fei Ma (XJTLU)
Co-supervisor: Dr. Linglong Yuan (UoL)
Cameras can perceive the appearance, color, and shape information of objects in the environment and are widely used in object classification, object detection, object segmentation, object tracking and other fields. However, it has drawbacks in recognizing the distance and speed of an object. Radar (refers to millimeter-wave radar in this project) uses the Doppler principle to measure the distance and speed of the object, and is not affected by external factors in the weather. Therefore, the combination of camera and radar can realize the perception of all-round attributes of the object and can meet the needs of working in all-weather conditions. At present, with the rapid development of deep learning technology, multi-sensor fusion has attracted more and more attention. However, the technology of radar-camera fusion is far from mature, especially the multi-modal object association, object detection and tracking technology in the moving environment, which has also become a bottleneck restricting the wide application of radar-camera fusion technology. This project takes radar-camera fusion as the research object and conducts systematic research on the perception of the environment by the radar-camera, the processing and understanding of sensor information, and the mapping of perception information to the decision. Besides, this project applies the method of deep learning to the fusion of radar and camera to establish a unified deep learning model to analyze the data of radar and camera. In addition, this project will also be combined with actual scenarios to develop products of radar-camera integration in intelligent transportation, assisted driving, consumer electronics, security monitoring, robots and other industries.
For more information about doctoral scholarship and PhD programme at Xi’an
Jiaotong-Liverpool University (XJTLU): Please visit
http://www.xjtlu.edu.cn/en/study-with-us/admissions/entry-requirements
http://www.xjtlu.edu.cn/en/admissions/phd/feesscholarships.html
Requirements:
The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in Signal Processing, Telecommunications, Computer science and Machine Learning. Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.
Degree:
The student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of the program.
Supervisor Profile:
Principal Supervisor:
Link of Profile: https://www.xjtlu.edu.cn/en/departments/academicdepartments/communications-and-networking/staff/limin-yu
JITRI co-supervisor:
Link of Profile: https://www.xjtlu.edu.cn/zh/staff?department=jitri&alias=baojiang-li
How to Apply:
Interested applicants are advised to email: [Email Address Removed] (XJTLU principal supervisor’s email address) or [Email Address Removed] the following documents for initial review and assessment (please put the project title in the subject line).
- CV
- Two reference letters with company/university letterhead
- Personal statement outlining your interest in the position
- Proof of English language proficiency (an IELTS score of 6.5 or above)
- Verified school transcripts in both Chinese and English (for international students, only the English version is required)
- Verified certificates of education qualifications in both Chinese and English (for international students, only the English version is required)
- PDF copy of Master Degree dissertation (or an equivalent writing sample) and examiners reports available
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