Print Email Facebook Twitter Multi-target detection using Relevance Vector Machine Title Multi-target detection using Relevance Vector Machine Author Sharma, Sumeet (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Driessen, J.N. (mentor) Yarovoy, Alexander (graduation committee) Hendriks, R.C. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Date 2023-02-23 Abstract The main purpose of a radar is to detect, recognize, and track objects of interest. When it is known that only a single target is present, the matched filter is proven to be optimal detector. However, in practice, a radar scene often consists of multiple targets. For example, in air surveillance and monitoring applications, multiple aircrafts might be in the airspace. When multiple targets are to be detected the matched filter is not guaranteed to give the best results. This can happen when a strong reflector masks the signals reflected from weak reflectors, thereby resulting in missed detections. Furthermore, when the sensor resolution is low, targets that are spaced closely together may only result in a single target actually being detected. This research explores how the Relevance Vector Machine (RVM) framework may be used to achieve a better multi-target detector than the commonly used basic matched filter approach. RVM was selected to resolve the multi-target detection problem as it estimates the target locations iteratively. In this research it was shown how the RVM framework can be used to model the fluctuation of swerling I/II targets. Additionally, the RVM algorithm was modified to incorporate a notion of statistical thresholding. Simulations show that using RVM the false alarm rate can be reduced and target locations can be more accurately recovered compared to other existing methods in case of multiple swerling I/II fluctuating targets. Furthermore, the proposed approach is shown to have a much lower convergence time compared to a similar expectation-maximization based method, namely Enhanced Sparse Bayesian Learning. Subject Multi-target detectionStatistical thresholdingRelevance vector machine To reference this document use: http://resolver.tudelft.nl/uuid:ad4179dd-a1c7-40b3-b166-214acc16d9f3 Part of collection Student theses Document type master thesis Rights © 2023 Sumeet Sharma Files PDF MSc_Thesis_Sumeet_Sharma_ ... 022023.pdf 839.44 KB Close viewer /islandora/object/uuid:ad4179dd-a1c7-40b3-b166-214acc16d9f3/datastream/OBJ/view